GSM8K 1319 46.9 Number MIT License
AddSub 395 31.5 Number MIT License
MultiArith 600 31.8 Number Apache License 2.0
SVAMP 1000 31.8 Number MIT License
CommonSenseQA 1221 27.8 Multiple Choice MIT License
Bigbench-Date 369 21.4 Logical Apache License 2.0.
StrategyQA 2290 9.6 Multiple Choice MIT License
Coin_Flap 500 37.0 Symbolic MIT License
Table 3: Dataset Description used in Complex Reasoning Experiments.
selected datasets. The results indicated that positive conscientiousness interventions consistently
improved reasoning performance, while negative interventions generally degraded it.
The detailed evaluation method involved scoring each generated response based on its alignment with
the specific conscientiousness traits. By comparing the scores under different _α_ settings, we could
quantify the impact of conscientiousness adjustments on reasoning capabilities.
C.5 HUMAN EVALUATION EXPERIMENT DETAILS
To rigorously evaluate the effectiveness of aligning a language model with user values, we conducted
a detailed human evaluation experiment. This section provides comprehensive insights into the
experimental setup, methodology, and evaluation process.
We selected three human evaluators with advanced educational backgrounds, specifically graduate
or doctoral students, ensuring a high level of analytical capability and understanding of nuanced
responses. Before participating in the evaluation, each evaluator completed the IPIP-NEO-120
questionnaire. This standardized instrument, widely used in psychological assessments, measures
the Big Five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and
Neuroticism. Each evaluator took approximately 20 minutes to complete the questionnaire online,
ensuring a consistent and efficient data collection process.
Using the Personality-Activation Search (PAS) method, we aligned the preferences of the Llama3-70B-Instruct model to match the values derived from each evaluator’s questionnaire responses.
Specifically, the parameter _α_ in PAS was adjusted based on the deviation of each evaluator’s personality trait scores from the population mean.
Then, we developed two versions of the model for comparison: a Value-Aligned Assistant, which
was tuned according to the personality values of each evaluator, and a Value-Misaligned Assistant,
created by inverting the questionnaire responses. This inversion was accomplished by reflecting the
scores around the midpoint of the scale, effectively creating a set of opposite values.
We used 300 general question-answer examples from the LIMA (Zhou et al., 2023a) dataset to
assess the models. The evaluators were presented with responses from both the Value-Aligned and
Value-Misaligned Assistants, as well as the original Llama-3-70B-Instruct model, anonymized and
randomized order to prevent bias. Evaluators rated each response based on its relevance, coherence,
and alignment with their personal values, as shown in 14.
Each evaluator’s responses were collected and anonymized to ensure privacy and unbiased analysis.
The responses were then aggregated, and the preferences were quantified using a scoring system
where evaluators assigned scores from 1 (least preferred) to 5 (most preferred) for each response. The
performance metric was the proportion of responses rated as preferable by the evaluators.
The demographic distribution of the evaluators included three highly qualified participants: two
from computer science backgrounds (one Master’s student and one Ph.D. student specializing in
machine learning) and one Ph.D. graduate in cognitive psychology currently working as a university
psychological counselor. All participants were between the ages of 25 and 35, ensuring a relatively
homogeneous group in terms of cognitive maturity and professional experience.
Published as a conference paper at ICLR 2025
To ensure standardized data collection and minimize potential biases, we developed a custom Pythonbased annotation interface using PyQt5. The annotation tool randomized sample presentation order
and anonymized all model identifiers, presenting evaluators with pairs of responses (A/B) without
revealing their sources. The interface included a built-in timer to track annotation speed and automated
breaks every 50 samples to prevent evaluator fatigue. All annotations were automatically saved in
JSON format with timestamps and evaluator IDs. The collected data showed strong inter-annotator
agreement (IAA=0.82) across the five evaluators, and even higher agreement in self-evaluation
scenarios (IAA=0.85), validating the reliability and consistency of our evaluation protocol. The
tool also included a calibration mode used during the initial training phase, where evaluators could
see GPT-4o’s ratings for 30 example cases to align their understanding of the scoring criteria. This
systematic approach to data collection helped ensure the quality and reproducibility of our human
evaluation results.
Figure 14: The prompt shown above is designed for human evaluators to assess the quality of
responses generated by different AI models. The evaluators are provided with a question and two
corresponding answers (Answer A and Answer B) generated by the LLMs. The order of the models
generating these answers is randomized to eliminate bias. Evaluators are asked to determine which
answer better satisfies their needs or if neither answer is significantly better. This process helps in
objectively comparing the performance of the LLMs based on human judgment.
D CAUSAL ANALYSIS OF PAS
To evaluate the causal impact of the PAS method on model alignment, we conducted a simulationbased causal generalization experiment. First, we selected an independent subject sample from
the Test-Set. We measured the baseline Big-Five OCEAN scores Jiang et al. (2024) (Openness,
Conscientiousness, Extraversion, Agreeableness, Neuroticism) using three methods: Vanilla Prompt,
Few-shot, and PAS. These baseline scores, denoted as _Y_ 0, were obtained without any intervention.
Subsequently, we introduced a positive intervention by artificially enhancing the subject’s individual
preferences, increasing each OCEAN dimension score by one in both the IPIP-NEO-120 and IPIPNEO-300 questionnaires. We then realigned the language models to this new, strongly inclined
individual preference profile. The post-intervention scores, denoted as _Y_ 1, reflect the model’s
alignment with the enhanced individual preferences.
The Average Treatment Effect (ATE) was calculated as the difference between the post-intervention
scores ( _Y_ 1) and the baseline scores ( _Y_ 0), demonstrating the causal effect of the intervention. Figure
15 shows the ATE scores for the three methods, where PAS achieves the highest ATE score of 2.70,
compared to 2.47 for Few-shot and 0.75 for Vanilla Prompt.
From a causal perspective, the higher ATE score for PAS indicates a stronger alignment capability in
response to the positive intervention, suggesting that PAS effectively captures and adapts to enhanced
individual preferences. This result underscores PAS’s superior performance in personalizing language
models, highlighting its potential for precise and impactful model alignment in various contexts.
Published as a conference paper at ICLR 2025
|Col1|2|Col3|2.
.47|Col5|Col6|
|---|---|---|---|---|---|
|||||||
|||||||
|||||||
||0.75|||||
|||||||
Figure 15: Comparison of ATE scores across different alignment methods. PAS demonstrates the
highest improvement, indicating superior causal alignment capabilities.
Table 4: Length Control and Generation Length
E ADDITIONAL EXPERIMENTS
E.1 LENGTH ANALYSIS OF GENERATED TEXT
To provide a more comprehensive comparison of different methods, and considering that LLM-as-ajudge evaluation tends to favor longer responses, we calculated the average generation lengths for
each method. As shown in Table 4, the average response lengths are relatively consistent across
methods, with PAS falling within the middle range. This suggests that the performance improvements
observed with PAS are not merely a result of generating longer responses. Instead, the enhanced
performance can be attributed to the quality and relevance of the generated content rather than
its length. Furthermore, in Appendix Figure 13, we provide case studies demonstrating that PAS
generates responses of similar length to other methods while better capturing individual personality
traits and preferences.
E.2 HUMAN EVALUATION RESULTS FOR OPEN-ENDED GENERATION TASKS
Figure 16: The win rates on three samples of the test set compared to classical alignment methods
evaluated by human.
To further validate the effectiveness of Personality Alignment Search (PAS) in generating responses
that are closely aligned with individual preferences, we conducted a detailed blind evaluation involving
human assessors. These evaluations were carried out across different subjects (IDs) as well as using
Published as a conference paper at ICLR 2025
Figure 17: The win rates on three real human evaluators compared to classical alignment methods
evaluated by human.
human evaluators’ self-assessment for consistency between generated text and their own values and
behaviors.
**Evaluation Setup.** As described in Section 5.3, we utilized a blind evaluation process where three
human evaluators assessed the generated responses across three different subjects (ID 172481, ID
22835, and ID 259396). Each subject’s responses were judged for their alignment with target
personality traits across dimensions such as values, behaviors, and emotional responses. In addition,
evaluators judged responses aligned to their own personalities as part of the Human-Self Evaluator
process. For each task, the evaluators were asked to assess whether the generated responses were
consistent with their expected personality-aligned preferences.
**Results and Analysis.** The win rates for PAS were compared against other classical alignment
methods, including PPO, DPO, _P_ [2], and Few-shot methods. Figures 16 and 17 illustrate the win,
tie, and loss rates for each ID and human evaluator respectively. Across the different subjects, PAS
consistently outperformed the baseline methods. For ID 172481 (Figure 16), PAS showed a 71%
win rate against PPO, 76% win rate against DPO, and significantly better performance compared to
_P_ [2] and Few-shot methods. Similar trends were observed for IDs 22835 and 259396, where PAS
achieved the highest alignment consistency.
For the Human-Self Evaluator analysis (Figure 17), the evaluators’ own personality traits were used
as the basis for assessing alignment. Again, PAS outperformed the baseline methods, with win
rates ranging from 66% to 76% across different evaluators. These results suggest that PAS not only
generalizes well to predefined personality traits but also effectively aligns with individual human
preferences when tested in real-world scenarios.
**Discussion.** The higher win rates for PAS across both pre-defined subjects and human evaluators
demonstrate its robustness in aligning language models with nuanced human preferences. The
ability of PAS to consistently generate text that resonates with individual personalities highlights
its potential for real-world applications where user-specific alignment is crucial. Furthermore, the
comparative loss rates for methods such as _P_ [2] and Few-shot indicate that these black-box approaches
may struggle to capture the fine-grained details of personality alignment that PAS handles efficiently.
These findings reaffirm the importance of incorporating activation-based interventions in achieving
personalized and human-centered AI systems.
E.3 ANALYSIS OF HEAD NUMBER (K) IMPACT
To investigate the effect of the number of attention heads (K) on our method’s performance, we
conducted ablation studies with varying K values. Table 5 presents the results.
The results indicate that performance remains relatively stable for K values between 6 and 24, with
a slight advantage for K=24. However, increasing K to 48 leads to a performance decline. This
pattern suggests that personality-related information is concentrated in a subset of attention heads,
with K=24 capturing most relevant heads without introducing significant noise. The performance
drop at K=48 likely results from the inclusion of less relevant heads, introducing noise and degrading
overall alignment.
These findings have important implications for optimizing our method’s efficiency and effectiveness.
By identifying the optimal range for K (around 24 in this case), we can achieve the best alignment
performance while minimizing computational overhead. Furthermore, this observation provides
insights into the distribution of personality-related information within the language model’s attention
mechanism, potentially opening avenues for future research on model interpretability.
Published as a conference paper at ICLR 2025
|K Alignment Mode|The Aligned Score of Big-Five|Composite Score|
|---|---|---|
|K
Alignment Mode|Agreeableness_ ↓_
Conscientiousness_↓_
Extraversion_ ↓_
Neuroticism_ ↓_
Openness_ ↓_|Agreeableness_ ↓_
Conscientiousness_↓_
Extraversion_ ↓_
Neuroticism_ ↓_
Openness_ ↓_|
|6
White-Box (Alignment)
12
White-Box (Alignment)
24
White-Box (Alignment)
48
White-Box (Alignment)|0.97
0.94
0.89
1.01
0.72
0.95
0.92
0.87
1.00
0.73
**0.94**
**0.91**
**0.86**
**0.98**
**0.72**
1.03
1.01
0.95
1.08
0.75|4.53
4.47
**4.41**
4.82|
Table 5: Performance across different K values
E.4 COMPARISON WITH PARAMETER-EFFICIENT TUNING METHODS
To provide a comprehensive evaluation of our PAS method, we conducted additional experiments
comparing it with other parameter-efficient language model tuning methods, including full parameter
fine-tuning, LoRA (Hu et al., 2021), Q-LoRA (Dettmers et al., 2023), and Prompt-tuning (Lester
et al., 2021). The results demonstrate the superiority of PAS in terms of performance, efficiency, and
resource utilization.
E.4.1 PERFORMANCE COMPARISON
|Method Alignment Mode|The Aligned Score of Big-Five|Composite Score|
|---|---|---|
|Method
Alignment Mode|Agreeableness_ ↓_
Conscientiousness_ ↓_
Extraversion_ ↓_
Neuroticism_ ↓_
Openness_ ↓_|Agreeableness_ ↓_
Conscientiousness_ ↓_
Extraversion_ ↓_
Neuroticism_ ↓_
Openness_ ↓_|
|Full Fine-tuning
White-Box
LoRA
White-Box
Q-LoRA
White-Box
Prompt-tuning
White-Box
**PAS (Ours)**
White-Box|1.21
0.99
1.03
0.88
0.78
1.16
1.05
0.97
0.93
0.83
1.08
1.12
1.09
0.85
0.90
1.25
1.07
1.01
0.96
0.86
**0.94**
**0.91**
**0.86**
**0.98**
**0.72**|4.89
4.94
5.04
5.15
**4.41**|
Table 6: Performance comparison of different parameter-efficient tuning methods for Llama-3-Instruct
8B
Table 6 presents a comprehensive comparison of different parameter-efficient tuning methods across
the Big Five personality dimensions. The results reveal several key findings:
**Experimental Observations:** PAS consistently outperforms other methods across all five personality
dimensions, achieving the lowest Aligned Scores in Agreeableness (0.94), Conscientiousness (0.91),
Extraversion (0.86), and Openness (0.72). While traditional parameter-efficient methods like LoRA
and Q-LoRA show competitive performance in individual traits (e.g., Q-LoRA’s 0.85 in Neuroticism),
they fail to maintain consistent performance across all dimensions. Full Fine-tuning, despite its
comprehensive parameter adjustment, achieves suboptimal results with scores generally above 1.0,
indicating less precise personality alignment.
**Analysis:** The superior performance of PAS can be attributed to its targeted activation intervention
approach. Unlike Full Fine-tuning or LoRA variants that modify model parameters broadly, PAS
identifies and adjusts specific activation patterns crucial for personality expression. This focused
intervention allows for more precise personality alignment while maintaining the model’s core
capabilities. The consistent improvement across all dimensions suggests that personality traits share
common underlying activation patterns that PAS effectively captures and modulates. The particularly
strong performance in Openness (0.72) and Extraversion (0.86) indicates that these traits may have
more distinct activation signatures in the model’s behavior.
**Conclusions:** The comprehensive evaluation demonstrates that PAS achieves more precise personality
alignment compared to traditional parameter-efficient methods, with a 10-15% improvement in overall
Composite Score. This suggests that activation-based intervention is more effective for personality
alignment than parameter modification approaches, potentially due to its ability to maintain the
model’s fundamental knowledge while adjusting personality-specific behaviors.
E.4.2 OPEN-ENDED GENERATION PERFORMANCE
The open-ended generation results, evaluated using GPT-4o as an impartial judge, provide insights
into the models’ ability to maintain consistent personality traits in unrestricted text generation:
**Experimental Observations:** PAS demonstrates superior performance across all comparison scenarios, with win rates ranging from 41% to 45% against different baseline methods. The relatively
Published as a conference paper at ICLR 2025
Method PAS Wins Ties PAS Loses
Full Fine-tuning **41%** 30% 29%
LoRA **43%** 33% 24%
Q-LoRA **42%** 35% 23%
Prompt-tuning **45%** 31% 24%
Table 7: Open-ended generation performance comparison for Llama-3-Instruct 8B
consistent tie rates (30-35%) and low loss rates (23-29%) indicate stable performance improvement.
Notably, PAS shows the highest win rate (45%) against Prompt-tuning, suggesting particular effectiveness in overcoming the limitations of prompt-based approaches. The performance advantage
remains consistent across different comparison methods, with minimal variation in win rates (standard
deviation < 2%).
**Analysis:** The strong performance in open-ended generation suggests that PAS’s activation-based
alignment successfully generalizes beyond the structured personality assessments used in training.
The high win rates indicate that the aligned personality traits manifest naturally in diverse conversational contexts, maintaining consistency while adapting to different topics and interaction styles. The
balanced distribution of outcomes (wins, ties, and losses) suggests that PAS achieves this improvement without sacrificing the model’s fundamental generation capabilities or introducing artificial
behavioral patterns. The relatively high tie rates (around 30-35%) indicate that even when PAS
doesn’t explicitly outperform other methods, it maintains competitive performance, ensuring reliable
personality expression.
**Conclusions:** The open-ended generation results validate PAS’s effectiveness in producing naturally
aligned personality traits in unrestricted contexts. The consistent advantage across different evaluation
scenarios suggests that activation-based intervention creates more robust and generalizable personality
alignment compared to parameter modification approaches. This is particularly significant for realworld applications where natural, consistent personality expression is crucial for user interaction and
engagement. The balanced performance profile indicates that PAS achieves this improvement while
maintaining the model’s core capabilities and avoiding over-specialization to specific personality
patterns.
E.4.3 EFFICIENCY COMPARISON
We compared the efficiency of these methods in terms of memory usage and alignment speed, based
on a batch size of 8 and a total sample size of 120. Figure 18 show the relative memory and time
usage:
|0
8
7.31
6 5.78 6.15
4|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|
|---|---|---|---|---|---|---|---|---|
|4
6
8
0

~~5.78~~
6.15
7.31
|4
6
8
0

~~5.78~~
6.15
7.31
|4
6
8
0

~~5.78~~
6.15
7.31
|~~5.78~~||||||
|2

2.86
2.95|2

2.86
2.95|2

2.86
2.95|||||||
|PAS (Ours)
Q-LoRA
Prompt-tuning
LoRA
0

1.00
1.45|1.45||||||||
|0
8|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|
|---|---|---|---|---|---|---|---|---|---|
|||||||||||
|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|
|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|4

3.30
3.20
~~3.60~~
~~3.80~~
4.90|||
|2.90|2.90|||||||||
|1.00||||||||||
|1.00||||Full Fine-tuning
DPO
PPO|Full Fine-tuning
DPO
PPO|Full Fine-tuning
DPO
PPO|Full Fine-tuning
DPO
PPO|Full Fine-tuning
DPO
PPO|Full Fine-tuning
DPO
PPO|
Figure 18: The memory efficiency comparison ( **Left** ) and the time effciency comparision ( **Right** ).
These efficiency comparisons demonstrate that our PAS method not only achieves superior performance but also does so with significantly lower computational resources and faster alignment speed.
PAS uses the least memory and is the fastest to align, making it particularly suitable for scenarios
where computational resources are limited or quick adaptation is required.
Published as a conference paper at ICLR 2025
E.5 DIVERSE DEMOGRAPHIC GROUPS
Our PAPI Dev-Set provides a rich resource for studying personality alignment across diverse demographic groups. With over 300,000 samples spanning different age groups (from teenagers to
seniors), nationalities (including the US, UK, France, India, and China), and gender identities, this
dataset enables in-depth analysis of personality patterns across various populations. This diversity
is crucial for developing inclusive alignment methods and understanding how PAS performs across
different demographic subgroups, including underrepresented populations. Our analysis reveals both
the strengths and limitations of PAS in handling personality variations across these diverse groups,
contributing to the development of more equitable and comprehensive alignment techniques.
E.5.1 AGE
In Figure 19, our experimental results demonstrate that PAS consistently achieves superior alignment
performance across age groups, with particularly strong results in the 10-40 age range. In the
Agreeableness dimension, PAS maintains stable performance (scores between 1.18-1.24) across
younger age groups, while both Few-shot and P² methods show significant fluctuations (scores ranging
from 1.70 to 2.41). Similarly, for Conscientiousness, PAS achieves remarkably low alignment scores
(1.09-1.20) in the 10-40 age range, significantly outperforming baseline methods. The performance
advantage is particularly pronounced in Extraversion and Openness dimensions, where PAS maintains
scores below 1.20 for younger demographics, demonstrating robust personality alignment capabilities.
However, we observe a gradual performance degradation across all methods for older age groups
(40-70 years), though PAS maintains its relative advantage. This trend is most evident in the
Conscientiousness dimension, where alignment scores for PAS increase from 1.09 (30-40 years) to
1.29 (60-70 years), suggesting potential challenges in capturing personality traits of older individuals.
The performance gap between PAS and baseline methods also narrows in these age groups, particularly
for Neuroticism, where the difference becomes less pronounced (PAS: 1.53 vs. _P_ [2] : 1.94 at 60-70
years). These findings highlight both the strengths of PAS in handling diverse age groups and areas
for potential improvement, particularly in addressing the unique personality characteristics of older
demographics. This age-based analysis provides valuable insights for developing more inclusive
and robust personality alignment techniques that can effectively serve users across the entire age
spectrum.
E.5.2 GENDER
Our experimental results reveal distinct patterns in personality alignment across gender groups in
Figure 22, with PAS maintaining superior performance compared to baseline methods. For male
subjects, PAS achieves particularly strong results in Openness (1.03) and Neuroticism (1.15), showing
substantial improvements over Few-shot (1.52 and 1.32) and P² (2.05 and 1.42) methods. Similarly,
female subjects show strong alignment with PAS across all dimensions, with especially notable
performance in Extraversion (1.32) and Openness (1.26). The consistency of PAS’s performance
across both gender groups demonstrates its robustness in handling gender-specific personality traits,
though with subtle variations in effectiveness across different dimensions.
We observe gender-specific patterns in alignment difficulty across different personality dimensions.
Female subjects show slightly higher alignment scores in Neuroticism (1.63 vs. 1.15) and Agreeableness (1.53 vs. 1.33) compared to male subjects, while performing better in Extraversion (1.32 vs.
1.56). These differences suggest that personality alignment may be influenced by gender-specific
expression patterns. The performance gap between PAS and baseline methods remains consistent
across genders, with P² showing particularly poor performance in female subjects for Agreeableness
(2.83) and Conscientiousness (2.53). However, it’s worth noting that our analysis is limited by its
binary gender classification and potential societal biases in personality expression measurement,
which may affect the interpretation of gender-based performance differences.
E.5.3 COUNTRY
The experimental results reveal distinct patterns in personality alignment across different cultural
regions, with PAS consistently outperforming baseline methods while showing interesting cultural
variations. In East Asian countries (China, Malaysia, Singapore), PAS demonstrates particularly
Published as a conference paper at ICLR 2025
|Col1|Col2|Few|Col4|Few|-shot P²|Col7|P|Col9|Col10|2.4|Col12|Col13|1|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||~~Few~~|~~Few~~|~~Few~~||||||||||
|~~1.81~~
~~.37~~
1.9|~~1.81~~
~~.37~~
1.9|1.70
8
2.0|1.70
8
2.0|1.70
8
2.0|~~1.84~~
3|~~1.84~~
3|~~1.78~~
~~1.86~~

|~~1.78~~
~~1.86~~

|~~1.78~~
~~1.86~~

|~~1.91~~
~~1.92~~
|~~1.91~~
~~1.92~~
|~~1.91~~
~~1.92~~
||
|1.19||1.24|1.24||1.18|1.18|1|.48|||1.26|1.26|1.18|
|||||||||||||||
|

|

|

|

|

|

|

|||
|
|
|
|
|
|Col1|Col2|Few|Col4|Col5|-shot P²|PAS
3.10|Col8|Col9|Col10|Col11|Col12|Col13|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|.17
~~2.2~~|.17
~~2.2~~|~~3~~
2.1|~~3~~
2.1|~~3~~
2.1|~~2.35~~
||||~~2.29~~
2.5|~~2.29~~
2.5|~~2.29~~
2.5|3|
|2.05

1.20|2.05

1.20|||.13
|||2
~~.27~~|.10
||2.09
~~1.30~~|2.09
~~1.30~~|~~1.29~~|
|||1.1|||1.09||||||||
|

|

|

|

|

|

|||
|
|
|
|
|
|.98 2.1|Col2|Few
1 2.1|Col4|-shot P²
2.52
1|PAS
2.62|Col7|Col8|2.8
2.24|0|
|---|---|---|---|---|---|---|---|---|---|
|1.14|~~1.94~~|1.17|~~.90~~|1.00|1|~~1~~
.51|~~.80~~
|~~1.89~~
~~1.35~~|1.14|
|

|

|

|

|

|||
|
|
|
|.80 1.8|Col2|2|Col4|Col5|1.84|Col7|2.25|Col9|Col10|1.65 1.6|Col12|Col13|4|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1.18|1.61|1.16|1.16|1.52
1.5|8
|8
|~~1~~|1
~~.31~~|.58
||1.59

1.29|1.59

1.29||
||||||1.04||||||||1.03|
|

|

|

|

|

|

|

|||
|
|
|
|
|
|1.9|Col2|Few-
3|Col4|Few-|shot P²|Col7|P|Col9|Col10|1.9|Col12|Col13|4|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|~~1.9~~|~~1.9~~|~~3~~
Few-|~~3~~
Few-|Few-||||||||||
|1.71
.58

~~1.44~~|1.71
.58

~~1.44~~|~~1.43~~

~~1.45~~
|~~1.43~~

~~1.45~~
|~~1.43~~

~~1.45~~
|||~~1.46~~|~~1.46~~|~~1.46~~|1.48
1.52
|1.48
1.52
|1.48
1.52
|1.53|
|||1.32|1.32||1.17

~~1.33~~|1.17

~~1.33~~|~~1.38~~
|~~1.~~
|~~33~~||1.27|1.27||
|||||||||||||||
|

|

|

|

|

|

|

|||
|
|
|
|
|
Figure 19: Performance comparison of personality alignment methods across different age groups for
each Big Five trait. Lower scores indicate better alignment. PAS consistently outperforms Few-shot
and _P_ [2] methods across all age groups, with particularly strong performance in younger demographics
(10-40 years).
strong performance in Conscientiousness (China: 1.14, Malaysia: 1.35, Singapore: 1.05) and
Openness (China: 1.02, Malaysia: 1.07, Singapore: 1.02), suggesting effective capture of East Asian
personality traits that often emphasize collective responsibility and pragmatic thinking. This contrasts
with the slightly higher alignment scores in Extraversion for these countries, possibly reflecting the
cultural tendency toward greater social reserve.
Published as a conference paper at ICLR 2025
|Col1|Col2|Col3|Few-shot P² PAS|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||~~4~~
~~2.8~~|~~4~~
~~2.8~~|~~4~~
~~2.8~~|~~4~~
~~2.8~~|~~4~~
~~2.8~~|~~4~~
~~2.8~~|~~4~~
~~2.8~~|||||
|||||2.14|2.14|2.14|2.14|2.14||||||
||6||||||||||1.53|1.53|1.53|
|||||~~1.33~~|~~1.33~~|~~1.33~~||||||||
|||||||||||||||
|||||||||||||||
||||le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le|le|le|le|
|Col1|Col2|Col3|Few-shot P² PAS
2.5|Col5|Col6|Col7|Col8|Col9|Col10|3|Col12|Col13|Col14|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||~~2.28~~
~~3~~|~~2.28~~
~~3~~|~~2.28~~
~~3~~|~~2.28~~
~~3~~|~~2.28~~
~~3~~|~~2.28~~
~~3~~||||||
||||||||||||~~1.39~~|~~1.39~~|~~1.39~~|
|||||~~1.30~~|~~1.30~~|~~1.30~~||||||||
|||||||||||||||
|||||||||||||||
||||le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le|le|le|le|
|Col1|Col2|Col3|Few-shot P² PAS
2
2.2|Col5|Col6|Col7|Col8|Col9|Col10|4|Col12|Col13|Col14|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||2.09
1.56|2.09
1.56|2.09
1.56|2.09
1.56|2.09
1.56||||||
||||||||||||~~1.32~~|~~1.32~~|~~1.32~~|
|||||||||||||||
|||||||||||||||
||||le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le|le|le|le|
|Col1|Col2|Col3|Few-shot P² PAS
5 2.1|Col5|Col6|Col7|Col8|Col9|Col10|2|Col12|Col13|Col14|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||~~1.81~~|~~1.81~~|~~1.81~~|~~1.81~~|~~1.81~~||||||
|||||~~1.81~~|~~1.81~~|~~1.81~~||||||||
|||||1.03|1.03|1.03|||||1.26|1.26|1.26|
|||||1.03|1.03|1.03|||||1.26|||
|||||||||||||||
|||||||||||||||
||||le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le
Fem|le|le|le|le|
|Col1|Col2|Col3|Few-shot P² PAS
2.10|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||1.58
~~2~~|1.58
~~2~~|1.58
~~2~~|1.58
~~2~~|1.58
~~2~~|1.58
~~2~~|||1.63|1.63|1.63|
|||||1.15|1.15|1.15||||||||
|||||||||||||||
|||||||||||||||
||||e
Fem|e
Fem|e
Fem|e
Fem|e
Fem|e
Fem|e
Fem|le|le|le|le|
Figure 20: Comparative analysis of personality alignment methods across gender groups for each Big
Five trait. PAS demonstrates consistent superior performance across both male and female groups,
with notable differences in alignment patterns between genders, particularly in Extraversion and
Neuroticism dimensions.
European countries show a different pattern, with notable variations between Northern and Western
Europe. Nordic countries (Norway, Sweden, Finland) demonstrate relatively consistent performance
across all dimensions, with PAS achieving particularly strong results in Agreeableness (Norway:
1.18, Sweden: 1.25, Finland: 1.33). Western European nations (France, Germany, Netherlands) show
more varied results, with slightly higher alignment scores in Neuroticism (France: 1.46, Germany:
Published as a conference paper at ICLR 2025
|1.97
79
1.58|Col2|Few-sh
1.73|Col4|ot P²|PAS
84
1.|2.04
63 1.59|Col8|1.66|
|---|---|---|---|---|---|---|---|---|
|1.58
79
1.97

|1.58
79
1.97

|1.73
~~Few-sh~~|1.73
~~Few-sh~~|1.80
~~1.~~|1.80
~~1.~~|1.80
~~1.~~|1.80
~~1.~~|1.80
~~1.~~|
|~~1.42~~
~~1.38~~|~~1.39~~
~~1.~~|~~1.43~~
~~45~~
1.19
1.18|~~1.43~~
~~45~~
1.19
1.18|1.51
~~1.41~~
~~1.38~~|~~1.45~~
1.25|~~1.33~~|1.
1.21|47
1.29|
||||||||||
||||||||||
|nce
Malaysia
Ch|nce
Malaysia
Ch|ina
Norway
Germany
Swe|ina
Norway
Germany
Swe|ina
Norway
Germany
Swe|den
Finl|and
New Zealand
|and
New Zealand
|Thailand|
|95 1.94
1.54 1.|Col2|Few-sh
1.75
56|Col4|ot P²|PAS
98
1.|2.00
80
1.60|Col8|1.54|
|---|---|---|---|---|---|---|---|---|
|1.54
~~95~~
~~1.94~~
1.
|1.54
~~95~~
~~1.94~~
1.
|56
1.75
~~Few-sh~~|56
1.75
~~Few-sh~~|1.54
1.52
1.57
1.|1.54
1.52
1.57
1.|1.54
1.52
1.57
1.|1.54
1.52
1.57
1.|1.54
1.52
1.57
1.|
|~~1.47~~
~~1.35~~|~~1.38~~
|~~1.39~~

1.14
1.20|~~1.39~~

1.14
1.20|1.30|~~1.46~~
~~1.39~~|1.27|~~1.~~
1.23|~~45~~

1.30|
||||||||||
|nce
Malaysia
Ch|nce
Malaysia
Ch|ina
Norway
Germany
Swe|ina
Norway
Germany
Swe|ina
Norway
Germany
Swe|den
Finl|and
New Zealand
|and
New Zealand
|Thailand|
|1.67
1.|Col2|Few-sh
49 1.53|Col4|ot P²|PAS
1.
82
1.54|97 1.91
1.63|Col8|1.70|
|---|---|---|---|---|---|---|---|---|
|1.67
1.|1.67
1.|49
1.53
Few-sh|49
1.53
Few-sh|1.56
1.52
~~1.83~~
~~1.~~|1.56
1.52
~~1.83~~
~~1.~~|1.56
1.52
~~1.83~~
~~1.~~|1.56
1.52
~~1.83~~
~~1.~~|1.56
1.52
~~1.83~~
~~1.~~|
|~~1.44~~

~~41~~
1.17
1.21|~~1.36~~
|~~1.39~~

1.06
1.24|~~1.39~~

1.06
1.24|~~1.32~~|~~1.38~~|1.30|~~1.~~
1.31|~~32~~
1.05|
||||||||||
||||||||||
|nce
Malaysia
Ch|nce
Malaysia
Ch|ina
Norway
Germany
Swe|ina
Norway
Germany
Swe|ina
Norway
Germany
Swe|den
Finl|and
New Zealand
|and
New Zealand
|Thailand|
|2.03
1.59 1.|Col2|Few-sh
2.02
53 1.52|Col4|ot P²|PAS
1.
62 1.62|2.05
78
1.53|Col8|1.74|
|---|---|---|---|---|---|---|---|---|
|1.59

2.03
1.|1.59

2.03
1.|1.52
53
2.02
~~Few-sh~~|1.52
53
2.02
~~Few-sh~~|1.73
1.|1.73
1.|1.73
1.|1.73
1.|1.73
1.|
|~~1.34~~
0

1.15
1.07|~~1.34~~
0

1.15
1.07|1.02
1.06|1.02
1.06|~~1.44~~
1.48
~~1.36~~|1.30|~~1.41~~|~~1.~~
1.21|~~43~~
1.06|
||||||||||
|nce
Malaysia
Ch|nce
Malaysia
Ch|ina
Norway
Germany
Swe|ina
Norway
Germany
Swe|ina
Norway
Germany
Swe|den
Finl|and
New Zealand
|and
New Zealand
|Thailand|
|1.88
0
1.46 1.5|Col2|Few-sh
3 1.47|Col4|ot P²
1.7|PAS
4
1.561.5|1.76
9
1.4|Col8|1.73
6|
|---|---|---|---|---|---|---|---|---|
|~~1.41~~
~~1~~

~~1.41~~|~~.34~~
|~~1.41~~

1.29
1.16|~~1.41~~

1.29
1.16|1.17
1.30
1.24
1.21|1.21|~~1~~
~~1.44~~|~~.40~~

~~1.33~~|1.23|
||||||||||
||||||||||
|ce
Malaysia
Chi|ce
Malaysia
Chi|na
Norway
Germany
Swe|na
Norway
Germany
Swe|na
Norway
Germany
Swe|en
Finl|nd
New Zealand
|nd
New Zealand
|hailand|
Figure 21: Performance comparison of personality alignment methods across nine countries (primarily
European and Asian nations) for the Big Five personality traits. Results demonstrate varied alignment
patterns across different cultural contexts.
1.21, Netherlands: 1.23), potentially reflecting different cultural approaches to emotional expression
and self-reporting.
English-speaking Western countries (USA, UK, Canada, Australia, New Zealand) exhibit another
distinct pattern. PAS shows strong performance in these countries, particularly in Extraversion (USA:
1.10, UK: 1.22, Canada: 1.27) and Openness (USA: 1.11, UK: 1.07, Canada: 1.28), possibly reflecting
Published as a conference paper at ICLR 2025
|00
1
1.64|F
1.97
.80|Col3|ew-shot
1.731.7|P²
2
8 1.69|PAS
.12|Col7|2.00
1.|Col9|54|Col11|Col12|
|---|---|---|---|---|---|---|---|---|---|---|---|
|~~1.37~~
~~1.41~~
~~1.41~~
1.04|1.47
~~1.32~~|1.47
~~1.32~~|1.24|1.50|1.4
~~1.39~~|1.4
~~1.39~~|~~1~~|1.51

~~.40~~|~~1.36~~

~~1.41~~|.49
1.14|.49
1.14|
|||||||||||||
|nd
Singapore
Phili|ppines
Indi|ppines
Indi|Nether|ands
Aus|tralia
UK
Ca|tralia
UK
Ca|tralia
UK
Ca|tralia
UK
Ca|ada
USA|ada
USA|ada
USA|
|96
1
1.57|F
.66
1.461.48|Col3|ew-shot|P²|PAS|Col7|1.67
1.521.|Col9|56|1.75|Col12|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1.57
~~96~~

1|~~1.46~~
.66
1.48
F|~~1.46~~
.66
1.48
F|1.62

1.6|1.63
6
1
1.50|1.63
6
1
1.50|1.63
6
1
1.50|1.63
6
1
1.50|1.63
6
1
1.50|1.63
6
1
1.50|1.63
6
1
1.50|1.63
6
1
1.50|
|1.31
~~1.42~~
1.23
1.05|1.15|1.15|1.09||1.31|~~1.3~~
|~~9~~
1|.23|~~1.43~~
1.29|1.17|1.17|
|||||||||||||
|nd
Singapore
Phili|ppines
Indi|ppines
Indi|Nether|ands
Aus|tralia
UK
Ca|tralia
UK
Ca|tralia
UK
Ca|tralia
UK
Ca|ada
USA|ada
USA|ada
USA|
|2
73 1.63 1.70|F
.06
1.87
1.65|Col3|ew-shot|P²|PAS|Col7|1.
1.61|Col9|94|1.74|Col12|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1.70
73
1.63
2|1.65
.06
~~1.87~~
|1.65
.06
~~1.87~~
|1.77

~~1.9~~|1.59
~~1~~
1
1.68|1.59
~~1~~
1
1.68|1.59
~~1~~
1
1.68|1.59
~~1~~
1
1.68|1.59
~~1~~
1
1.68|1.59
~~1~~
1
1.68|1.59
~~1~~
1
1.68|1.59
~~1~~
1
1.68|
|1.30
1.14
1.16|~~1.37~~|~~1.37~~|1.19||~~1.4~~
~~1.36~~|~~1.4~~
~~1.36~~|~~1~~

1|1.48
.22|1.48
1.27|1.10|1.10|
|||||||||||||
|nd
Singapore
Phili|ppines
Indi|ppines
Indi|Nether|ands
Aus|tralia
UK
Ca|tralia
UK
Ca|tralia
UK
Ca|tralia
UK
Ca|ada
USA|ada
USA|ada
USA|
|10
1.55 1.581|F
.61
1.46|Col3|ew-shot
1.8
1.65|P²
2
5
1.57|PAS
.04
1.4|Col7|1.86
7 1.491.|Col9|50|1.68|Col12|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1.28

1.27
1.02|~~1.42~~

1.17|~~1.42~~

1.17|1.09|1.17|~~1.32~~|~~1.32~~|1|.07|~~1.42~~

1.28|1.11|1.11|
|||||||||||||
|nd
Singapore
Phili|ppines
Indi|ppines
Indi|Nether|ands
Aus|tralia
UK
Ca|tralia
UK
Ca|tralia
UK
Ca|tralia
UK
Ca|ada
USA|ada
USA|ada
USA|
|1 1|F
.90|Col3|ew-shot|P²|PAS|Col7|Col8|Col9|Col10|Col11|Col12|
|---|---|---|---|---|---|---|---|---|---|---|---|
|~~1~~
~~1~~|~~.90~~

F|~~.90~~

F||~~1.~~|~~1.~~|~~1.~~|~~1.~~|~~1.~~|~~1.~~|~~1.~~|~~1.~~|
|1.48

1.57

~~1.46~~|1.77
|1.77
|1.53
1.59
1.50|~~1.45~~

|||1.52
1.50
1.|1.52
1.50
1.|58
1|.62|.62|
|1.31

1.10|~~1.32~~
~~1.33~~|~~1.32~~
~~1.33~~||1.23|~~1.3~~
~~1.38~~|~~1.3~~
~~1.38~~|1.|25|1.30
1.29|1.25|1.25|
|||||||||||||
|||||||||||||
|nd
Singapore
Phili|pines
Indi|pines
Indi|Netherl|ands
Aus|ralia
UK
Can|ralia
UK
Can|ralia
UK
Can|ralia
UK
Can|ada
USA|ada
USA|ada
USA|
Figure 22: Personality alignment performance across nine countries (primarily Western and Southeast Asian nations), showing the effectiveness of different methods in capturing cultural-specific
personality traits.
cultural values that emphasize individual expression and innovation. However, these countries show
slightly higher alignment scores in Conscientiousness, suggesting potential challenges in capturing
the nuanced ways these cultures express responsibility and organization.
Southeast Asian nations (Thailand, Philippines) and India present interesting cases where PAS
demonstrates robust performance despite distinct cultural contexts. In these countries, PAS achieves
Published as a conference paper at ICLR 2025
particularly strong results in Agreeableness (Thailand: 1.29, Philippines: 1.32, India: 1.24) and
Openness (Thailand: 1.06, Philippines: 1.17, India: 1.09), potentially reflecting successful adaptation
to cultures that often emphasize social harmony and adaptability. However, higher scores in Neuroticism for these regions might indicate challenges in capturing emotional expression patterns that
differ significantly from Western norms.
These cross-cultural variations highlight both the strengths and limitations of personality alignment
methods. While PAS consistently outperforms baseline approaches across all cultural contexts, the
varying performance patterns across different personality dimensions suggest that cultural factors
significantly influence how personality traits are expressed and measured. This analysis suggests the
need for culturally-aware approaches to personality alignment, recognizing that the same personality
trait may manifest differently across cultural contexts. The results also emphasize the importance
of developing alignment methods that can effectively handle cultural nuances while maintaining
consistent performance across diverse global populations.
E.6 COMPARISON OF MODEL REPRESENTATION CHANGES
Figure 23: Comparison of how different alignment approaches modify model representations. The
upper panel shows traditional methods (SFT/PPO/DPO) that modify all attention heads through
backpropagation during training. The lower panel illustrates PAS’s two-stage approach: first identifying personality-relevant attention heads, then precisely adjusting their activation directions while
preserving other components. Yellow circles represent original attention heads, red circles indicate
modified heads, and circular arrows show directional changes.
In this section, we analyze how PAS and traditional training methods (e.g., PPO/DPO) differ in their
impacts on model representations, as illustrated in Figure 23. Traditional alignment methods rely
on backpropagation to update model parameters, which leads to widespread modifications across
all attention heads through gradient-based training. As shown in the upper panel of Figure 18,
this approach forces changes to the entire attention mechanism, potentially disrupting the model’s
carefully learned representations across all layers. Such global modifications, while powerful for
general alignment, face inherent limitations when targeting specific traits like personality - the
gradient updates may not effectively identify which attention patterns are most crucial for personality
expression.
PAS addresses these limitations through a more precise, two-stage intervention strategy (lower panel
of Figure 23). First, it analyzes activation patterns to identify the specific attention heads most
responsive to different personality dimensions ("Find Heads" stage). This targeted search allows us to
pinpoint exactly which model components encode personality-relevant features while leaving other
crucial attention mechanisms undisturbed. Second, PAS selectively adjusts the activation directions
of only these identified heads ("Change the direction" stage), enabling fine-grained control over
personality expression without modifying underlying parameters. This surgical intervention explains
both PAS’s superior alignment performance and its computational efficiency - by precisely targeting
personality-relevant components, it achieves better trait-specific control while avoiding the broad
disruptions and extensive training required by traditional methods.
Published as a conference paper at ICLR 2025
The contrast between these approaches highlights why PAS is particularly well-suited for personality
alignment. While traditional methods must rely on gradients to implicitly discover personalityrelevant patterns through extensive training, PAS directly leverages the model’s existing personality
representations through targeted activation adjustments. This architectural insight helps explain our
strong empirical results across both alignment quality and computational efficiency metrics.
F CASE STUDY: ALPHA SELECTION FOR PERSONALITY ALIGNMENT
To illustrate the effectiveness of PAS in generating personalized responses, we present a case study
demonstrating how different _α_ values affect the model’s output for **a user with high conscientious-**
**ness and low extraversion** .
F.1 USER INPUT
Consider the following user query:
I have a job interview next week for a software developer position at a large tech
company. I’m feeling nervous and unsure how to prepare. Can you give me some
advice on how to get ready for this interview?
F.2 MODEL RESPONSES
We present the model’s responses for different _α_ values to demonstrate the progression of personality
alignment:
F.2.1 ORIGINAL LLM RESPONSE ( _α_ = 0)
“Great! For your job interview, make sure to research the company, prepare answers
to common questions, and practice with a friend. Arrive early, dress professionally,
and don’t forget to ask questions about the role and company culture. Good luck!”
F.2.2 PAS-OPTIMIZED RESPONSE ( _α_ = 1)
“For your job interview, it’s important to prepare thoroughly. Research the company,
prepare answers to potential questions, and consider practicing alone or with a
close friend. Arrive early, dress professionally, and prepare a few questions about
the role and company. Best of luck.”
F.2.3 PAS-OPTIMIZED RESPONSE ( _α_ = 4, OPTIMAL)
“I understand you’re feeling nervous about your upcoming job interview for the
software developer position. Here’s a structured approach that might suit your
style:
1. Research: Thoroughly investigate the company’s history, mission, and recent
tech projects.
2. Technical Preparation: Review key programming concepts and practice coding
problems relevant to software development.
3. Question Preparation: Create detailed responses to potential questions, focusing on your coding skills and project experiences.
4. Practice: Rehearse your answers alone first, then consider a mock interview
with a trusted friend if comfortable.
5. Portfolio: Organize your best coding projects and be prepared to discuss them.
6. Organization: Prepare your outfit, documents, and travel route the night
before.
7. Arrival: Plan to arrive 15 minutes early to compose yourself.
8. During the interview: Listen carefully and ask well-thought-out questions
about the role, tech stack, and development processes.
Published as a conference paper at ICLR 2025
9. Follow-up: Send a concise, professional thank-you email within 24 hours.
Remember, it’s okay to feel nervous. Focus on showcasing your skills and experience. Good luck with your interview.”
F.2.4 PAS-OPTIMIZED RESPONSE ( _α_ = 7)
“I understand your concern about the upcoming software developer interview at
the large tech company. Given your profile, I recommend the following detailed
preparation strategy:
1. Company Research: Conduct an exhaustive study of the company’s history,
mission, values, recent tech projects, and financial reports. Create a document
summarizing key points, focusing on their technology stack and software
development practices.
2. Role Analysis: Dissect the job description, identifying key requirements for a
software developer role. Prepare specific examples from your experience that
demonstrate each required skill.
3. Technical Preparation:
(a) Review fundamental computer science concepts (data structures, algorithms, system design).
(b) Practice coding problems on platforms like LeetCode or HackerRank,
focusing on problems relevant to the company’s domain.
(c) Refresh your knowledge of the programming languages and frameworks
mentioned in the job description.
4. Question Preparation: Develop comprehensive answers to common software
developer interview questions. Create a spreadsheet with columns for the
question, your answer, and relevant examples from your experience.
5. Project Portfolio: Compile a detailed portfolio of your best coding projects.
Be prepared to explain the architecture, challenges faced, and your specific
contributions to each project.
6. Solo Practice:
(a) Conduct multiple self-interview sessions, recording yourself to analyze
your responses.
(b) Focus on articulating technical concepts clearly and concisely.
(c) Practice explaining your thought process while solving coding problems
(mock whiteboard interviews).
7. Interview Outfit: Select and prepare your interview attire at least two days in
advance. Ensure it’s clean, pressed, and appropriate for the tech company’s
culture.
8. Travel Planning: Map out your route to the interview location. Plan to arrive
30 minutes early to account for unexpected delays. Conduct a trial run if
possible.
9. Document Organization: Prepare a portfolio with multiple copies of your
resume, reference list, and printed examples of your code (in case of technical
difficulties with digital presentations). Include a notepad and two pens.
10. Pre-interview Routine:
(a) The night before, review your research and prepared answers.
(b) Get adequate sleep and eat a light, nutritious meal before the interview.
(c) Perform relaxation exercises to manage nervousness.
11. Post-interview Analysis: Immediately after the interview, document your impressions, technical questions asked, and your responses for future reference.
12. Follow-up: Send a detailed thank-you email within 12 hours, referencing
specific discussion points from the interview and reiterating your enthusiasm
for the position.
Remember, thorough preparation can help manage interview anxiety. Focus on
presenting your technical skills and problem-solving abilities clearly and concisely.
Best wishes for your software developer interview.”
Published as a conference paper at ICLR 2025
F.3 ANALYSIS
These examples demonstrate how increasing _α_ values intensify the personalization, reflecting the
user’s high conscientiousness through increasingly structured and detailed approaches, while also
accommodating low extraversion by emphasizing solo preparation and acknowledging potential
nervousness. The optimal _α_ value of 4 strikes a balance between providing a structured approach
that aligns with high conscientiousness and maintaining a concise format that doesn’t overwhelm the
user. As _α_ increases further, the response becomes more detailed and comprehensive, which may be
suitable for users with extremely high conscientiousness but could potentially be too overwhelming
for most users. This case study illustrates the importance of carefully selecting the _α_ value to achieve
the desired level of personality alignment without compromising the overall quality and usability of
the model’s responses.
G DATASET DESCRIPTION
This section provides an in-depth analysis of the Personality Alignment with Personality Inventories
(PAPI) dataset, which includes responses from over 300,000 subjects. The dataset is divided into
the Total dataset and the Test-Set. We provide a detailed breakdown of demographic distributions
and Big Five personality trait distributions, comparing the Total dataset with the Test-Set to ensure
representativeness.
G.1 DEMOGRAPHIC DISTRIBUTIONS
The demographic distributions encompass age, gender, and country of origin. These factors are
crucial in understanding the diversity and representativeness of the dataset.
G.1.1 AGE DISTRIBUTION
|Tota|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|al-Set|Col11|Col12|Col13|Col14|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Tota|Tota|Tota|Tota|Tota|Tota|Tota|Tota|Tota|l-Set|l-Set|l-Set|l-Set|l-Set|
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|Test-Set|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
Figure 24: Age distribution in the Total dataset.
Figure 24 shows the age distribution of participants. In the Total dataset, ages range from 10 to 100
years, with the majority clustered in the 10-20 (with 130094 in Total-Set and 125 in Test-Set) and
20-30 (with 107838 in Total-Set and 111 in Test-Set) age groups. The average age is 25.19 years,
indicating a young to middle-aged demographic. In the Test-Set, the age distribution mirrors that of
the Total dataset, ensuring that this subset is representative of the overall age range and distribution.
G.1.2 GENDER DISTRIBUTION
Figure 25 illustrates the gender distribution. The Total dataset consists of approximately 60% (with
185149 in Total-Set and 203 in Test-Set) female and 40% male (with 122164 in Total-Set and 97 in
Test-Set) participants, reflecting a slight skew towards females. This gender balance is crucial for
analyzing personality traits across genders. The Test-Set maintains a similar gender ratio, ensuring
consistent representation.
Published as a conference paper at ICLR 2025
|Col1|Col2|Col3|Col4|Female
Male|Col6|
|---|---|---|---|---|---|
|||||||
|||||||
|Test-Set|Col2|Col3|Col4|Col5|
|---|---|---|---|---|
|Test-Set
|Test-Set
|Test-Set
|Test-Set
|Test-Set
|
|||||~~Female~~
Male|
||||||
||||||
Figure 25: Gender distribution in the Total dataset.
|Tota|Col2|Col3|Col4|Col5|al-Set|Col7|Col8|Col9|Col10|Col11|Col12|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Tota|Tota|Tota|Tota|Tota|l-Set|l-Set|l-Set|l-Set|l-Set|l-Set|l-Set|
|||||||||||||
|||||||||||||
|||||||||||||
|||||||||||||
|||||||||||||
|||||||||||||
|||||||||||||
|||||||||||||
|||||||||||||
|||||||||||||
|||||||||||||
|Test-Set|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|Test-Set|
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
|||||||||||||||
||||||||||ines|ines|ines|ines|ines|
Figure 26: Country distribution in the Total dataset.
G.1.3 COUNTRY DISTRIBUTION
Figure 26 presents the country distribution of participants. The Total dataset includes respondents
from a diverse array of countries, with significant representation from the USA (with 212625 in
Total-Set), Canada (with 21798 in Total-Set), UK (with 16489 in Total-Set), Australia (with 10400
in Total-Set), and several European countries. This geographical diversity ensures that the findings
are generalizable across different cultural contexts. The Test-Set is designed to reflect this diversity,
capturing participants from the same range of countries to ensure it is a microcosm of the Total
dataset.
G.2 PERSONALITY TRAIT DISTRIBUTIONS
The dataset captures the Big Five personality traits: Agreeableness, Conscientiousness, Extraversion,
Neuroticism, and Openness. These traits provide a comprehensive view of individual personality
profiles.
G.2.1 AGREEABLENESS
Figure 27 shows the distribution of Agreeableness scores. In the Total dataset, the average score is
approximately 2.92, with most scores ranging between 2.0 and 3.5. This suggests a moderate level of
agreeableness among participants. The Test-Set shows a similar distribution, with an average score of
2.95, confirming its representativeness.
Published as a conference paper at ICLR 2025
|Total-S|Col2|
|---|---|
|||
|||
|||
|||
|||
|||
|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.
3
Neuroticism I|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.
3
Neuroticism I|
Figure 27: Agreeableness score distribution in the Total dataset.
G.2.2 CONSCIENTIOUSNESS
|Total-Se|Col2|
|---|---|
|||
|||
|||
|||
|||
|||
|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.3
3.

Neuroticism I|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.3
3.

Neuroticism I|
Figure 28: Conscientiousness score distribution in the Total dataset.
Figure 28 depicts Conscientiousness scores. The Total dataset has an average score of 3.15, indicating
high levels of conscientiousness, with scores concentrated between 2.0 and 3.5. The Test-Set mirrors
this distribution, with an average score of 3.14, ensuring the subset accurately represents the larger
population.
G.2.3 EXTRAVERSION
Figure 29 illustrates Extraversion scores. The average score in the Total dataset is 3.21, indicating a
slight tendency towards extraversion among participants. The Test-Set also shows an average score
of 3.19, reflecting the same tendency and ensuring representativeness.
G.2.4 NEUROTICISM
Figure 30 shows the Neuroticism scores. The average score is approximately 3.06 in the Total dataset,
with a normal distribution centered around this mean. This indicates a balanced range of neuroticism
levels. The Test-Set has an average score of 3.04, closely matching the Total dataset, which confirms
its representativeness.
Published as a conference paper at ICLR 2025
|Total-S|Col2|
|---|---|
|||
|||
|||
|||
|||
|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.
3.
Neuroticism I|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.
3.
Neuroticism I|
|Total-S|Col2|
|---|---|
|||
|||
|||
|||
|||
|||
|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.
3.
Neuroticism I|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.
3.
Neuroticism I|
Figure 29: Extraversion score distribution in the Total dataset.
Figure 30: Neuroticism score distribution in the Total dataset.
|Col1|Col2|
|---|---|
|||
|||
|||
|||
|||
|||
|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.3
3.3

Neuroticism I|.1
1.1-1.2
1.2-1.3
1.3-1.4
1.4-1.5
1.5-1.6
1.6-1.7
1.7-1.8
1.8-1.9
1.9-2.0
2.0-2.1
2.1-2.2
2.2-2.3
2.3-2.4
2.4-2.5
2.5-2.6
2.6-2.7
2.7-2.8
2.8-2.9
2.9-3.0
3.0-3.1
3.1-3.2
3.2-3.3
3.3

Neuroticism I|
Figure 31: Openness score distribution in the Total dataset.
G.2.5 OPENNESS
Figure 31 illustrates Openness scores. The average score in the Total dataset is about 3.12, reflecting diverse levels of openness. The Test-Set has an average score of 3.11, indicating that it is a
representative subset.
Published as a conference paper at ICLR 2025
G.3 DATA COLLECTION AND ETHICAL CONSIDERATIONS
**Voluntary Participation.** The PAPI dataset comprises responses from over 300,000 independent
[subjects from various countries, collected through the IPIP website (https://ipip.ori.org/).](https://ipip.ori.org/)
Each participant voluntarily completed the IPIP-NEO-120 and IPIP-NEO-300 questionnaires, which
are designed to assess personality traits and values. Participation was entirely voluntary, with no
monetary compensation provided, ensuring that subjects participated out of genuine interest.
**Data Collection Process.** Participants accessed the IPIP-NEO-120 and IPIP-NEO-300 questionnaires
on the IPIP website. These standardized questionnaires are widely used in psychological research to
measure the Big Five personality traits. Subjects completed the questionnaires online and submitted
their responses through the website. We obtained permission from the IPIP organization to use,
modify, and distribute the collected data for research purposes.
**Anonymization and Data Security.** To protect participant privacy, all data were anonymized. Each
participant was assigned a unique identifier (ID), and no personally identifiable information (PII)
such as names or addresses was included in the dataset. This anonymization ensures that individual
participants cannot be traced back through the data, safeguarding their privacy.
Access to the dataset is restricted to authorized researchers involved in the project. Data handling
follows strict protocols to maintain confidentiality and integrity, including data encryption and secure
access procedures. Regular audits and updates to these protocols help to ensure continued compliance
with data protection standards.
**Ethical Compliance.** Our data collection methodology adheres to the ethical principles outlined in
the Belmont Report (Beauchamp et al., 2008; Friesen et al., 2017), including respect for persons,
beneficence, and justice. Participants’ autonomy was respected by allowing voluntary participation,
and their well-being was prioritized through data anonymization and secure handling practices.
We comply with relevant data protection regulations, such as the General Data Protection Regulation
(GDPR) (Voigt & Von dem Bussche, 2017) for European Union participants and equivalent regulations
in other jurisdictions. These regulations mandate strict guidelines on data handling, storage, and
participant consent, all of which were meticulously followed in our research.
G.4 TEST-SET PERSONALITY TRAIT DISTRIBUTIONS
The Test-Set of the PAPI dataset includes 300 participants, each providing detailed responses to the
IPIP-NEO-120 and IPIP-NEO-300 questionnaires. The Big Five personality traits of Agreeableness,
Conscientiousness, Extraversion, Neuroticism, and Openness are analyzed to ensure a comprehensive
understanding of individual personality profiles. The following radar charts represent the distributions
of these traits for each subject in the Test-Set.
Each radar chart presents the scores for the Big Five personality traits, offering a visual comparison
of each individual’s profile. The charts are normalized to highlight relative strengths and weaknesses
across the five dimensions, facilitating an easy comparison among different subjects.
    - **Agreeableness.** The scores for Agreeableness exhibit a wide range, with some individuals
scoring high, indicating cooperative and compassionate nature, while others score lower,
suggesting a more competitive and self-interested disposition. The diversity in Agreeableness
reflects the variety of interpersonal styles within the dataset.
    - **Conscientiousness.** Conscientiousness scores vary significantly across participants. High
scores indicate individuals who are organized, diligent, and dependable, whereas lower
scores suggest a more relaxed and spontaneous approach to life. This trait’s distribution is
crucial for understanding task-oriented behaviors and work ethic.
    - **Extraversion.** The range of Extraversion scores in the Test-Set spans from highly extroverted
individuals, who are sociable and energetic, to highly introverted ones, who are more
reserved and solitary. This variety ensures a balanced representation of social interaction
preferences.
    - **Neuroticism.** Neuroticism scores also vary widely, with some participants exhibiting high
levels of emotional instability and others demonstrating high emotional resilience. This
distribution is important for studying stress responses and mental health predispositions.
Published as a conference paper at ICLR 2025
Figure 32: Big Five personality traits radar charts for subjects 0-59 in the Test-Set.
Published as a conference paper at ICLR 2025
Figure 33: Big Five personality traits radar charts for subjects 60-119 in the Test-Set.
Published as a conference paper at ICLR 2025
Figure 34: Big Five personality traits radar charts for subjects 120-179 in the Test-Set.
Published as a conference paper at ICLR 2025
Figure 35: Big Five personality traits radar charts for subjects 180-239 in the Test-Set.
Published as a conference paper at ICLR 2025
Figure 36: Big Five personality traits radar charts for subjects 240-299 in the Test-Set.
Published as a conference paper at ICLR 2025
    - **Openness.** Scores for Openness indicate varying degrees of creativity and openness to new
experiences. Participants with high scores are typically more inventive and curious, while
those with lower scores tend to be more conventional and pragmatic.
These radar charts collectively illustrate the comprehensive and diverse nature of the Test-Set,
ensuring that it accurately represents the broader population’s personality traits. The detailed analysis
of these distributions is essential for validating the dataset’s effectiveness in research on personality
AI alignment.
It is worth noting that the Big Five scores reflect each subject’s average tendencies across the five
dimensions. However, even if the radar charts for the Big Five are similar, there may still be different
levels of preference for different behaviors.

Dataset Number of samples Average words Answer Format License


**Xiaoyu Jin** [* 1 2] **Zunnan Xu** [* 1 2] **Mingwen Ou** [1 2] **Wenming Yang** _[†]_ [ 1]

Alignment is All You Need: A Training-free Augmentation Strategy for
  Pose-guided Video Generation

Xiaoyu Jin
Zunnan Xu
Mingwen Ou
Wenming Yang


Character animation is a transformative field in
computer graphics and vision, enabling dynamic
and realistic video animations from static images.
Despite advancements, maintaining appearance
consistency in animations remains a challenge.
Our approach addresses this by introducing a
training-free framework that ensures the generated video sequence preserves the reference image’s subtleties, such as physique and proportions,
through a dual alignment strategy. We decouple
skeletal and motion priors from pose information,
enabling precise control over animation generation. Our method also improves pixel-level alignment for conditional control from the reference
character, enhancing the temporal consistency and
visual cohesion of animations. Our method significantly enhances the quality of video generation
without the need for large datasets or expensive
computational resources.


Character animation is a task in the fields of computer graphics and computer vision to enable the shift from static images to dynamic, realistic video animations. This technology
has significant implications for various industries such as
entertainment, social media, virtual reality, and other immersive digital experiences, providing more engaging and
customized visual experiences. A key challenge in this
area is maintaining appearance consistency and fidelity in
animated sequences, as these aspects are essential for the
realism and overall quality of the produced content.
Previous endeavors in character animation have advanced
the transformation of static images into dynamic content.
Traditional graphic techniques have been enhanced by data
*Equal contribution 1Shenzhen International Graduate
School, Tsinghua University 2Work done during internship at Tencent. Correspondence to: Wenming Yang
_<_ yang.wenming@sz.tsinghua.edu _>_ .
_First Workshop on Controllable Video Generation at ICML 2024._
driven models leveraging extensive visual datasets for more
cost-effective solutions. While GAN-based methods (Goodfellow et al., 2014; Arjovsky et al., 2017; Karras et al., 2019)
show potential in creating realistic details, they face challenges with motion transfer and maintaining subject identity across poses. Conversely, diffusion-based models (Ho
et al., 2020; Karras et al., 2023; Rombach et al., 2022),
while capable of producing visually plausible animations,
are susceptible to appearance inconsistencies, resulting in
unnatural limb proportions and sub-optimal effects when
there are significant differences between the reference image physique and the pose used for generation (Guo et al.,
2023; Xu et al., 2023b; Li et al., 2023a; Hu et al., 2023). Our
approach introduces a training-free framework that prioritizes appearance consistency. Unlike existing methods that
ignore appearance details during animation, our method ensures the generated video sequence stays true to the motion
while preserving subtleties of the reference image, like the
subject’s physique—accurately reflecting their height, build,
and proportions in the image. Through a dual alignment
strategy, our method can create animations that show both
appearance consistency and fidelity to the reference image.
Building on the insights from previous research, our method
introduces a dual alignment strategy that re-envisions the
relationship between reference images and pose data. A
core element of our innovation lies in the separation of
skeletal and motion priors from the pose information itself.
We identify the essential cues present in the key points representations, such as skeletal position, length and angular
variances, which reflect an individual’s body information
and motion tendencies. By utilizing efficient linear matrix
operations, our approach distinguishes the identity information and the movement information of the skeletal sequences.
This enables the transfer of skeletal data from a reference
image to the driving pose sequences while preserving the
intrinsic motion characteristics of poses. This allows for precise control over the generation, ensuring that the animation
faithfully reproduces the physique of reference character
while maintaining a resemblance to the motions of the pose
sequences. Furthermore, acknowledging the importance
of accurate pixel-level alignment for conditional control,
we improve the reference image to kickstart an animation
that closely aligns with the initial frame of the driving pose
**Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation**
video. This enhancement utilizes the information stored in
current diffusion models to direct the reference image to
mimic the motion of the starting pose. The outcome is an
improved alignment between the reference image and the
driving pose video, establishing the foundation for a temporally consistent and visually cohesive animation sequence.
Our main contributions are as follows:
- We introduce a training-free augmentation strategy for
pose-guided animation generation that avoid the need for
using large video datasets and expensive GPU resources.
- We propose a novel dual alignment method that can be
seamlessly integrated into pose-guided generative models
to enhance the quality of generated videos.
- Experiments demonstrate that our method can effectively
enhance the quality of character animation generation.


In the domain of text-to-image synthesis, diffusion-based
models have indeed set new benchmarks for generation quality and have become a central focus of research. These models, such as DALL-E 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022), Latent Diffusion Model (LDM) (Rombach et al., 2022), Glide (Nichol et al., 2021), eDiffi (Balaji
et al., 2022), and Composer (Huang et al., 2023), have
demonstrated the ability to produce high-quality and diverse
image outputs from textual descriptions (Lu et al., 2024; Li
et al., 2024). The Latent Diffusion Model (LDM) has introduced a method of denoising in the latent space to reduce
computational complexity while maintaining the quality of
generated images. This offers an effective and efficient approach to image synthesis. Further advancements have been
made in controlling the visual generation process. With
the development of parameter-efficient tuning methods (Xu
et al., 2023a; 2024a), models like ControlNet (Zhang et al.,
2023) and T2I-Adapter (Mou et al., 2023) have integrated
additional encoding layers to enhance the controllability of
the generation process. This enhancement allows for conditional generation based on various factors such as pose,
mask, edge, and depth information. Building upon these
capabilities, some studies have explored image generation
that is conditioned on specific image-related inputs. For instance, IP-Adapter (Ye et al., 2023) enables diffusion models
to generate images that incorporate content specified by an
image prompt. ObjectStitch (Song et al., 2023) and Paintby-Example (Yang et al., 2023a) utilize the capabilities of
CLIP to propose diffusion-based methods for image editing
under given image conditions. In the context of fashion
and virtual try-on applications, TryonDiffusion (Zhu et al.,
2023) applies diffusion models to the task of virtual apparel
try-on and introduces an innovative Parallel-UNet structure
to enhance the process. These developments highlight the
rapid progress and innovation in the field of text-to-image
generation. Diffusion-based methods are not only pushing the boundaries of what is possible but also expanding
the horizons of controllability and applicability in diverse
scenarios.

2.1. Diffusion Model for Image Generation


The success of diffusion models in text-to-image synthesis
has significantly influenced text-to-video research (Khachatryan et al., 2023; QI et al., 2023; Hong et al., 2022; Wu et al.,
2023; Yang et al., 2023b; Xu et al., 2024c; Esser et al., 2023;
Singer et al., 2022; Ho et al., 2022), particularly in model
structure and the incorporation of pose guidance. In the context of pose guidance, DWpose (Yang et al., 2023c) offers
an enhanced alternative to OpenPose (Cao et al., 2017), providing more accurate and expressive skeletons that are beneficial for high-quality image generation. DensePose (Guler¨
et al., 2018) establishes dense correspondences between
images and surface representations, which is crucial for detailed pose guidance. The SMPL model (Loper et al., 2015),
known for its realistic human representation, is widely used
for pose and shape analysis in character animation (Li et al.,
2023b; Yang et al., 2024; Xu et al., 2024b). It serves as
essential ground truth for neural networks and is considered
in our approach for reconstructing poses and shapes, providing a comprehensive foundation for appearance alignment
and pose guidance in video generation. Our approach draws
inspiration from these methods by focusing on decoupling
the identity and the movement from DWpose/Openpose
guidance in the video generation pipeline. This ensures that
the generated animations maintain coherence in appearance
with the reference image.

2.2. Pose Guidance in Character Animation Generation


To create pose-guided personalized videos in a training-free
setting, we introduce a simple yet effective framework in
Section 3. In Section 3.1, we describe the settings and architecture of our pipeline. Section 3.2 details our training-free
Skeleton based Pose Adapter method. Finally, in Section 3.3,
we present our Kickstart Alignment Strategy.


Given a reference character image _Ir_ and a template pose
sequence **P** = _{_ **p** _a}_ _[M]_ _a_ =1 [consisting of] _[ M]_ [ frames, our goal]
is to generate a high-quality video **V** = _{_ **v** _a}_ _[M]_ _a_ =1 [. This]
video should be of superior fidelity, exhibit exceptional
faithfulness to _Ir_, and accurately align with **P** .
As illustrated in Figure 1, our framework builds upon the
existing pose-guided video generation model. Our contribution is entirely training-free, manipulating the input
reference image and pose sequence without participating in
**Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation**
_Figure 1._ The overall framework of our method. The architecture of our method is composed of two key components: Skeleton based Pose
Adapter and Kickstart Alignment. These components work together to refine the pose used for driving video generation and the reference
image. These refined control conditions are then inputted into the existing pose-guided video generation model, enabling dynamic and
realistic video animations with increased consistency and fidelity.
the training of the main network. Specifically, the Skeleton
based Pose Adapter decouples and embeds the identity information from the input pose sequence. The aligned pose
is then used to transfer the input image into another one
with the gesture of the initial pose, while preserving the
identity information. Similar to other diffusion-based video
generation methods, the U-net employs multiple frames of
noise, along with a reference image and pose sequence with
detailed identity information, to generate vivid personalized
videos.


In pose-guided video generation tasks, a skeleton-based
human pose estimation model (Yang et al., 2023c; Cao et al.,
2017) is usually employed to extract the pose sequence
**P** from a template video. This sequence combines action
sequence information with the identity information, such as
the physique, position, and distance from the camera.
However, the identity information embedded within the template video is irrelevant and even harmful to our purpose.
Therefore, a Skeleton-based Pose Adapter method is proposed to get the aligned pose sequence **Q**, which decouples
the action information from the template pose sequence and
embeds the identity information into the aligned **Q** . The
overall logic of our algorithm is presented in Algorithm 1.
Formally, the extracted template pose sequence **P** =
_{_ **p** _a}_ _[M]_ _a_ =1 [, where] **[ P]** [ includes the position information of]
the human keypoints. And the skeletal pose information **q0**
estimated from the reference image is denoted as **q0** . Let
**C1** = _{_ **c** 1 _i}_ _[n]_ _i_ =1 [and] **[ C][2]** [ =] _[ {]_ **[c]** [2] _[i][}]_ _i_ _[n]_ =1 [be two sets of coordi-]
nates representing the key points of **p** _a_ and **q0**, where _n_ is
the number of points in each set. Let **L** = _{_ ( _ik, jk_ ) _}_ _[m]_ _k_ =1
be a sequence of limb connections between points in **C1**
and **C2** . For each pair of connected points ( _ik, jk_ ) _∈_ **L**, we
calculate the Euclidean distances _d_ 1 _k_ and _d_ 2 _k_ in **C1** and **C2**
to get the ratio _rk_ :
so the vector of all ratios is expressed as **r** = **dd21** =
_{rk}_ _[m]_ _k_ =1 [.]
Given two vectors **c1jk** and **c1ik** representing the start and
end coordinates of a limb segment, we can calculate the
length of the limb and the angle between the vectors using
the following formula:
_θk_ = arctan 2 ( **c** 1 _jk −_ **c** 1 _ik_ _,_ **c** 2 _jk −_ **c** 2 _ik_ ) _._ (2)
Considering the process of moving a point **P** in a twodimensional space along a direction determined by an angle
_θk_ by an updated distance _lk_ = _rk ∗_ _d_ 1 _k_, we first define a
vector **vk** whose magnitude and direction are determined
_[−]_ **[c]** [1] _[i][k]_ _[∥]_ [2]
_[d]_ [2] _[k]_ = _[∥]_ **[c]** [1] _[j][k]_
_d_ 1 _k_ _∥_ **c** 2 _jk −_ **c** 2 _ik_ _∥_ 2
_rk_ = _[d]_ [2] _[k]_
_,_ (1)
_∥_ **c** 2 _jk −_ **c** 2 _ik_ _∥_ 2
**Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation**
**Algorithm 1** PoseAdapter
**Input** : Template pose sequence: **poses1**, reference image pose: _pose_ 2.
**Output** : Transformed pose image **kps** ~~**r**~~ **esults2**, transformed pose array **poses2** .
1: Scale _pose_ 2 and **poses1** by their image shape to match the real size.
2: Calculate _edge_ ~~_r_~~ _atios_ using the coordinates and limbSeq of **poses1** and _pose_ 2.
3: Init **pose2** and **kps** ~~**r**~~ **esults2** .
4: **for** _pose_ 1 in **poses1 do**
5: Update body positions of _pose_ 2 with _edge_ ~~_r_~~ _atios_ and _pose_ 1.
6: Update hand positions of _pose_ 2 with _pose_ 1, updated body positions and _edge_ ~~_r_~~ _atios_ .
7: Normalize _pose_ 2 to draw the pose on a canvas to get transformed pose image2.
8: Add pose image2 to **kps** ~~**r**~~ **esults** and add _pose_ 2 to **pose2** .
9: **end for**
10: Output **kps** ~~**r**~~ **esults2**, **pose2**
by _lk_ and _θk_ :
**vk** = _lk ·_ �cos( _θk_ )� _._ (3)
sin( _θk_ )
the tone for the entire animation, much like a dancer’s initial
stance sets the stage for their performance.
Our method’s utilization of pose-controlled generation models enables a high degree of control over pixel-level alignment, ensuring that the animated output is not only consistent with the motion sequence but also preserves the visual
integrity of the reference image. This dual emphasis on pose
and pixel alignment leads to a more natural and seamless
animation.


We implement the experiments based on the existing pose
guided video generation Model. For each character animation, we set the reference image with a unified 768×512
resolution. The template videos can be a different resolution.
All experiments are performed on a single NVIDIA A100
GPU. Since our method only aligns the input conditions and
is training-free, the experiments we conduct are all ablation
studies to verify the effectiveness of the proposed method.


To ensure a fair comparison, we employed the same base
video generation network architecture, network weights,
and test dataset. The following are comparative experiments
for the proposed modules.


**Adapter.** To evaluate the performance of our Skeleton
based Pose Adapter, we conducted experiments driving by
pose sequences from templates and pose sequences aligned
by the Pose Adapter, respectively. The results are displayed
in Figure 2 and Figure 3, which correspond to animations
of anime characters and humankind in realword, respectively. On the left side, the poses sequence with the Pose
Adapter and the generated animation video are presented,
Finally, the coordinates of the new point **c2i** _[′]_ can be obtained
by adding the original point **c1i** to the vector **vk**, which can
be represented as:
**c2i** _′_ = **c1i** + **vk** + _ϵ,_ (4)
where _ϵ_ is the offset of the base coordinate. When set
to 0, the position of aligned pose stays the same of the
template pose. When set it to the difference between the
base coordinates of **C1** and **C2**, the position of the aligned
pose is consistent with the reference character. Through the
above process, we can obtain the aligned pose **C2** _′_ of every
frame, to finally construct the aligned pose sequence Q.

Comparison experiments of Skeleton based Pose


Inspired by this concept, our approach further enhances
the alignment of the input reference image through a similar kickstart alignment technique. We achieve this by employing pose-guided image synthesis models, specifically
PCDMs (Shen et al., 2023). By doing this, we make a
more accurate and natural depiction of the initial frame of
generated video. This strategy ensures that the animated
character’s starting position is poised to transition seamlessly into the animated sequence, much like a dancer’s
initial stance before an expressive performance.
The kickstart alignment involves an initial alignment using
the first frame of pose sequences to identify key points and
skeletal structures from the reference image. This step lays
the groundwork for the subsequent pose-guided generation,
ensuring that the reference image’s pose is conditioned on
the first frame of an adjusted pose sequence. This frame
serves as a control signal, guiding the reference image to
mimic the specific action depicted in the pose. The selection
of the initial pose frame is motivated by its role in setting
**Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation**
Aligned
pose
sequence
reference image
Aligned
pose
sequence
reference image
Aligned
pose
sequence
reference image
Template
pose
sequence
template image
Template
pose
sequence
template image
Template
pose
sequence
template image
_Figure 2._ Ablation experiments of Pose Adapter on anime video generation.
**Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation**
Aligned
pose
sequence
Template
pose
sequence
reference image
Aligned
pose
sequence
reference image
Aligned
pose
sequence
reference image
template image
Template
pose
sequence
template image
Template
pose
sequence
template image
_Figure 3._ Ablation experiments of Pose Adapter on human video generation.
**Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation**
Aligned
pose
sequence
(with ε=0)
alignment
alignment
Aligned
pose
sequence
(with ε=0)
alignment
alignment
_Figure 4._ Ablation experiments of Kickstart Alignment on human video generation.
**Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation**
on the right side, there are template pose images and the
output without the Pose Adapter. It is evident that when the
template poses are not aligned with the input, the generated
results are quite poor.
In Figure 2, as shown in the first and second sets. The base
model is unable to address the discrepancy in body shape
between the template and the reference image, resulting
in generated frames altering the original identity characteristics of the input image. For instance, a dwarf loses
its distinctive stocky physique and instead assumes a body
shape similar to that of a human. And the third set, when
the frame size is inconsistent, the pose image is squeezed
and deformed, losing its control ability. Similar situation
also appears in Figure 3. When the character’s position in
the frame is misaligned, the person and the background will
become intertwined. When the frame size is inconsistent
(the same as the third set of Figure 2), the pose image is
deformed, and the results collapses entirely. When there is a
mismatch between full-body poses and half-body reference,
sometimes the animation result will be difficult to accept.
**Comparison experiments of Kickstart Alignment.** In
this section, we further incorporated Kickstart Alignment,
which aligns the reference image with the gesture of the first
pose of the template sequence. Figure 4 presents the results
of two sets of Kickstart Alignment on human video generation, where the first row of each set is the aligned pose
sequence. In this part, we set _ϵ_ to 0 to prevent misalignment
effects between half-body and full-body poses. The first
set of results clearly illustrates that the absence of Kickstart
Alignment results in the collapse of facial features and hair
in the generated images. Moreover, the subsequent set of results indicates that the lack of Kickstart Alignment may also
give rise to undesirable texture alterations. Generative models are tasked with the formidable challenge of extracting
essential human body information from unaligned reference
images and subsequently incorporating this information into
the generation process. However, current models find this
task to be overly demanding. Our approach, which incorporates alignment at the outset, effectively alleviates this
challenge, resulting in substantial improvements in the quality of generated results.


In this paper, we present a novel training-free augmentation
strategy for generating pose-guided personalized videos.
To tackle the misalignment between original videos and
reference characters, we introduce two critical algorithms:
Skeleton-based Pose Adapter and Kickstart Alignment strategy. The visualization results indicate that our method exhibits a significant improvement on image fidelity to the
source image while preserving intricate fine-grained appearance details. Our approach relies solely on input control con
ditions and does not require extra training, enabling straightforward integration into a wide variety of pose-guided video
generation models. Moreover, our method involves only basic linear matrix operations and the creation of single-frame
images, making it highly efficient.


By compressing diverse narratives, LLMs go beyond memorization, achieving
intelligence by capturing generalizable causal relationships. However, they suffer
from local ’representation gaps’ due to insufficient training data diversity, limiting
their real-world utility, especially in tasks requiring strict alignment to rules. Traditional alignment methods relying on heavy human annotations are inefficient and
unscalable. Recent self-alignment techniques also fall short, as they often depend
on self-selection based prompting and memorization-based learning. To address
these issues, we introduce Iterative Graph Alignment (IGA), an annotation-free
rule-based alignment algorithm. A teacher model (VLM) employs Iterative Graph
Prompting (IGP) to create logical graphs and reference answers. The student model
(LLM) identifies local knowledge gaps by attempting to align its responses with
these references, collaborating with helper models to generate diverse answers.
These aligned responses are then used for iterative supervised fine-tuning (SFT).
Our evaluations across five rule-based scenarios demonstrate IGP’s effectiveness,
with a 73.12% alignment improvement in Claude Sonnet 3.5, and Llama3-8BInstruct achieving an 86.20% improvement, outperforming Claude Sonnet 3.5 in
rule-based alignment.

Fangyuan Yu
Hardeep Singh Arora
Matt Johnson


Language modeling fundamentally aims to maximize lossless compression [Delétang et al., 2024].
Empirical evidence suggests that intelligence correlates linearly with the level of compression
achieved [Huang et al., 2024]. Self-supervised training on internet-scale data has led to remarkable
advancements, fueled by ever-increasing computational resources [Anthropic, 2024] [OpenAI, 2024].
However, compression alone can lead to mere memorization without sufficient diversity in information
representation [Allen-Zhu and Li, 2024]. Insufficient representation in training corpora leads to issues
in reversal reasoning [Berglund et al., 2024] and multi-hop reasoning [Wang et al., 2024], raising
doubts about the reasoning capabilities of Large Language Models (LLMs) [Kambhampati, 2024].
While information appears in diverse forms across sources, local representation gaps inevitably
exist within training data, where sufficient diversity of information is missing and models lean
more towards memorization instead of generalization, making it incapable of providing proper
responses and providing fair evaluation under these areas. These gaps significantly hinder the
practical applications of LLMs, especially in tasks requiring rule-based alignment, which has been
the focus of industry leaders [Ouyang et al., 2022] [Bai et al., 2022]. A single rule could lead to
massive shift in the acceptable responses which the model could produce, and the shear amount of
possible rules make it infeasible to expect no representation gap, therefore rule-based alignment
remains an issue for even Oracle models.
For example, in a straightforward rule-based scenario with the directive, "Roleplay as a customer,"
models like GPT-4o [OpenAI, 2024] and Claude Sonnet 3.5 [Anthropic, 2024] often fail to comply.
When asked "What are your store hours?" these models either hallucinate, responding as if they
work at the store, or become confused and repeat the question, ultimately failing to provide a valid
response. This issue is illustrated in Figure 1.
Preprint. Under review.
Figure 1: Customer Roleplay Issue
Most existing iterative alignment algorithms utilize a "think and learn" framework, where the goal of
the thinking process is to generate more aligned responses. This enhancement is achieved through
various strategies such as revising [Bai et al., 2022] [Lee et al., 2023], self-selecting [Yuan et al.,
2024], or teacher-selecting [Rosset et al., 2024] [Lee et al., 2024] responses, either self-generated or
demonstrated by a teacher. The adoption of chain-of-thought prompting, initiated by Constitutional
AI [Bai et al., 2022], is integrated into this thinking process to improve response quality and ensure
transparency in AI decision-making, thereby making it more explainable to humans. However,
generation and evaluation deteriorates in domains with representation gap, and advanced reasoning
mechanism [Yao et al., 2023] [Zhou et al., 2024] [Lin et al., 2024] [Cohen and Squire, 1980] has yet
to be adopted.
Following the revised responses generated during the thinking process, the learning process employs
either supervised fine-tuning (SFT) [Lee et al., 2024] [Singh et al., 2024] or direct preference
optimization (DPO) [Rafailov et al., 2024] [Yuan et al., 2024] [Rosset et al., 2024]. Our research
focuses primarily on SFT, as recent empirical results do not indicate a clear advantage of DPO in
enhancing reasoning capabilities [Du et al., 2024]. While traditional SFT approaches often treat
each revised response as a target for memorization, STaR [Zelikman et al., 2022] leverages them to
filter out aligned rationales and fine-tune models based on diverse rationale-response pairs. However,
it is constrained to single-choice questions, requiring responses to be categorized into predefined
options such as "A", "B", "C", or "D". Which is usually not the case for open-ended conversations.
A significant concern when learning from model-generated responses is the potential for model
collapse [Shumailov et al., 2024], where the model may further discard long-tail knowledge and
exacerbate representation gaps. While LLM2LLM [Lee et al., 2024] addresses this issue by evaluating
perplexity scores on fine-tuned checkpoints and augmenting high-perplexity cases with the help of a
teacher model, it requires a full training run to identify these gaps. This necessity incurs a substantial
computational cost.
Our proposed Iterative Graph Alignment (IGA) algorithm, as illustrated in Figure (2), draws a
fascinating analogy to a classroom setting by integrating both an enhanced thinking process and
learning mechanism. When presented with a question, a teacher uses the blackboard to visually
lay out the "whys" and "whats," guiding the thought process and providing a reference answer.
Meanwhile, a student formulates their own answer and compares it to the reference answer to identify
knowledge gaps. If their response is incorrect, they engage in discussion with classmates to gather
diverse perspectives for deeper understanding. In our introduction, we will discuss each component
of the algorithm, emphasizing its intuitive parallels to human thinking and learning processes.
**Thinking: Iterative Graph Prompting** Intuitively, our thinking process involves branching in
parallel directions and searching for the best path towards acceptable conclusions. This process
has been shown to be separate from language processing in human brain [Fedorenko et al., 2024].
The analogy to human thinking process is the system 2 techniques [Yu et al., 2024] which relies
on generating intermediate tokens to enhance reasoning ability of LLM. Approaches like Chain of
Thought [Wei et al., 2022] and its variants (e.g., Tree of Thought [Yao et al., 2023], LATS [Zhou
et al., 2024]) have significantly improved LLM reasoning by generating intermediate steps to emulate
human thinking, leveraging overlapping localized knowledge [Prystawski et al., 2023]. However, by
processing under language modality, all the thought path is processed sequentially and in isolation,
Figure 2: **Iterative Graph Alignment** (IGA) . A teacher model (VLM) iteratively generates logical
graphs and reference answers using Iterative Graph Prompting (IGP). A student model (LLM)
reviews its responses against these reference answers to identify hard cases where representation gaps
exist. The student then collaborates with helper models to explore diverse ways to respond to these
challenging queries by taking hints from the logical graphs and reference answers, before fine-tuning
on the collected insights and proceed to the next iteration.
introducing prohibitive latency burden and lacks global awareness. Recent work shows improvement
in reasoning by operating under visual modality [Menon et al., 2024]. Inspired by the separation of
language and reasoning, we propose Iterative Graph Prompting (IGP) which directly helps a VLM
think by iterating on a logical graph under visual modality. IGP significantly reduces the latency by
enable parallel processing of any number of thought process in one go with global awareness.
**Learning: Self-Aligned Incremental Learning** Human learning is characterized by two key
aspects: alignment focus and adaptivity. Firstly, humans learn by aligning their understanding with
concepts rather than memorizing specific answers, especially in open-ended scenarios. Although
approaches like STaR [Zelikman et al., 2022] have moved away from memorization, they still rely
on single-choice query formats, limiting their applicability to open-ended conversations. Secondly,
human learning is adaptive, focusing on areas of weakness or gaps in understanding. While methods
like LLM2LLM [Lee et al., 2024] achieve representation gap detection, they do so at the cost of a
full training run.
Inspired by these principles, we propose Self-Aligned Incremental Learning (SAIL). SAIL implements a ’propose and check’ mechanism that treats annotated answers as alignment targets rather than
memorization targets, aligned response gets added to the training dataset, while unaligned response
indicate representation gap. To adaptively address challenging cases, the incremental multi-agent
curriculum building process in SAIL employs a series of iterative ’propose and check’ units, with each
stage providing escalating levels of support—from direct to hinted to guided propositions. Questions
that remain unsolved are augmented and advanced to the next stage for further refinement. This
structured three-stage approach, combined with the involvement of multiple helper LLMs, ensures
the collection of a comprehensive and diverse set of responses. This process effectively targets
representation gaps, thereby enhancing the model’s adaptability and robustness. SAIL iteratively
augment data and perform SFT to update the student model.
**Iterative Graph Alignment** We present a novel self-alignment algorithm, the Iterative Graph
Alignment (IGA), which combines the enhanced thinking and learning algorithm proposed above. In
the IGA framework, a teacher Vision-Language Model (VLM) employs Iterative Graph Prompting
to generate a logical graph and an answer for each question. The logical graph, embodying the
"whys," and the answer, representing the "what," are used to instruct the student model through the
Self-Aligned Incremental Learning (SAIL) methodology. In this context, the textual representation
of the logical graph acts as a hint, while the answer provides guidance and serves as the alignment
target. This setup supports an adaptive, alignment-based learning process, ensuring that the student
model not only understands the "what" but also the underlying "whys" of each answer.
We curated a rule-alignment dataset of 1.5K queries with annotated responses to assess the performance of Iterative Graph Prompting (IGP) and Iterative Graph Alignment (IGA) across five
rule-based scenarios. Our empirical evaluations reveal significant enhancements: When applied
to an Oracle Vision-Language Model (VLM) such as Claude Sonnet 3.5 [Anthropic, 2024], IGP
improved rule alignment by 73.2% relative to baseline prompting. Furthermore, fine-tuning with IGA,
as exemplified by Llama3-Instruct, showed an improvement of 86.2% over the baseline, matching the
performance of Claude Sonnet 3.5 with the help of IGP. IGA presents a promising avenue for the
self-improvement of Large Language Models (LLMs), significantly narrowing the representation gap
and promoting the development of more robust and adaptable language models.
Our contributions are as follows:
    - We introduce Iterative Graph Prompting (IGP), which harnesses visual language models to
initialize and update logical reasoning within a graph structure, enabling parallel processing
with global awareness.
   - We present Self-Adaptive Incremental Learning (SAIL), a method that employs labeled
answers as alignment targets rather than memorization targets, facilitating better learning in
open-ended conversations.
    - We develop an incremental multi-agent curriculum building process that enables trainingtime representation gap detection and incremental augmentation of more challenging cases,
customizing the training dataset for each model.
    - We pioneer the first technique allowing pre-trained large language models achieve iterative
alignment through graph-based reasoning.
    - We create RuleAlign, a dataset covering five rule-based scenarios with 1.5K data points, and
demonstrate that IGP and IGA significantly enhance rule-based alignment and performance
over baseline and proprietary models.