sapienzanlp
/

zebra-kb-csqa-train

+---
+license: cc
+language:
+- en
+base_model:
+- intfloat/e5-base-v2
+tags:
+- retrieval
+- question answering
+---
+<div align="center">
+  <img src="https://github.com/SapienzaNLP/zebra/assets/zebra.png?raw=true" height="150">
+</div>
+<div align="center">
+  <h1>ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering</h1>
+</div>
+<div style="display:flex; justify-content: center; align-items: center; flex-direction: row;">
+    <a href="https://2024.emnlp.org/"><img src="https://img.shields.io/badge/EMNLP-2024-4b44ce"></a> &nbsp; &nbsp;
+    <a href="https://arxiv.org/abs/placeholder"><img src="https://img.shields.io/badge/arXiv-placeholder-b31b1b.svg"></a> &nbsp; &nbsp;
+    <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><img src="https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg"></a> &nbsp; &nbsp;
+    <a href="https://huggingface.co/collections/sapienzanlp/zebra-66e3ec50c8ce415ea7572d0e"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Collection-FCD21D"></a> &nbsp; &nbsp;
+    <a href="https://github.com/SapienzaNLP/zebra"><img src="https://img.shields.io/badge/GitHub-Repo-121013?logo=github&logoColor=white"></a> &nbsp; &nbsp;
+</div>
+A retrieval augmentation framework for zero-shot commonsense question answering with LLMs.
+## 🛠️ Installation
+Installation from PyPi
+```bash
+pip install zebra
+```
+Installation from source
+```bash
+git clone https://github.com/framolfese/zebra.git
+cd zebra
+conda create -n zebra python==3.10
+conda activate zebra
+pip install -e .
+```
+## 🚀 Quick Start
+ZEBRA is a plug-and-play retrieval augmentation framework for **Commonsense Question Answering**. \
+It is composed of two pipeline stages: *knowledge generation* and *informed reasoning*. \
+The knowledge generation step is responsible for:
+- given a question, retrieving relevant examples of question-knowledge pairs from a large collection
+- prompting a LLM to generate useful explanations for the given input question by leveraging the relationships between the retrieved question-knowledge pairs.
+The informed reasoning step is responsible for prompting a LLM for the question answering task by taking advantage of the previously generated explanations.
+Here is an example of how to use ZEBRA for question answering:
+```python
+from zebra import Zebra
+# Load Zebra with language model, retriever, document index and explanations.
+zebra = Zebra(
+  model="meta-llama/Meta-Llama-3-8B-Instruct",
+  retriever="sapienzanlp/zebra-retriever-e5-base-v2",
+  document_index="sapienzanlp/zebra-kb"
+)
+# Provide a question and answer choices.
+questions = [
+    "What should you do if you see someone hurt and in need of help?",
+    "If your friend is upset, what is the best way to support them?",
+    "What should you do if your phone battery is running low in a public place?",
+    "What should you do if you are running late for an important meeting?",
+]
+choices = [
+    ["Walk away.", "Call for help.", "Take a photo for social media."],
+    ["Listen to them and offer comfort.", "Tell them they are overreacting.", "Ignore them and walk away."],
+    ["Borrow a stranger's phone.", "Use public charging station.", "Leave your phone unattended while it charges."],
+    ["Rush through traffic.", "Call and inform them you will be late.", "Do not show up at all."],
+]
+# Generate knowledge and perform question answering.
+zebra_output = zebra.pipeline(questions=questions, choices=choices)
+```
+The output contains, for each question, a list of generated explanations and the predicted answer:
+```bash
+  ZebraOutput(
+    explanations=[
+      [
+        "Walking away would be neglecting the person's need for help and potentially putting them in danger.",
+        'Calling for help, such as 911, is the most effective way to get the person the assistance they need.',
+        "Taking a photo for social media might spread awareness, but it's not a direct way to help the person in need."
+      ],
+      [
+        'Listening and offering comfort shows empathy and understanding.',
+        "Telling someone they're overreacting can be dismissive and unhelpful.",
+        'Ignoring someone in distress can be hurtful and unkind.'
+      ],
+      [
+        "Borrow a stranger's phone: Unwise, as it's a security risk and may lead to theft or damage.",
+        "Use public charging station: Safe and convenient, as it's a designated charging area.",
+        'Leave your phone unattended while it charges: Not recommended, as it may be stolen or damaged.'
+      ],
+      [
+        'Rush through traffic: This option is risky and may lead to accidents or stress.',
+        'Call and inform them you will be late: This is the most likely option, as it shows respect for the meeting and allows for adjustments.',
+        'Do not show up at all: This is unacceptable, as it shows disrespect for the meeting and may damage relationships.'
+      ],
+    ],
+    answers=[
+      "Call for help.",
+      "Listen to them and offer comfort.",
+      "Use public charging station.",
+      "Call and inform them you will be late."
+    ],
+  )
+```
+You can also call the `zebra.pipeline` method with the `return_dict` parameter set to `True` to ask ZEBRA to return also the retrieved examples along with their explanations.
+## Models and Data
+Models and data can be found at the following [HuggingFace Collection 🤗](https://huggingface.co/collections/sapienzanlp/zebra-66e3ec50c8ce415ea7572d0e).
+## 📊 Performance
+We evaluate the performance of ZEBRA on 8 well-established commonsense question answering datasets. The following table shows the results (accuracy) of the models before / after the application of ZEBRA.
+|          Model           |       CSQA      |      ARC-C      |      ARC-E      |       OBQA      |       PIQA      |       QASC      |      CSQA2      |        WG       |       AVG       |
+| ------------------------ | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- |
+| Mistral-7B-Instruct-v0.2 | 68.2 / **73.3** | 72.4	/ **75.2** | 85.8	/ **87.4** | 68.8	/ **75.8** | 76.1	/ **80.2** | 66.1	/ **68.3** | 58.5	/ **67.5** | 55.8 / **60.7** | 68.9 / **73.5** |
+| Phi3-small-8k-Instruct   | 77.2 / **80.9** | 90.4 / **91.6** | 96.9	/ **97.7** | 90.4	/ **91.2** | 86.6	/ **88.1** | **83.5**	/ 81.0 | 68.0	/ **74.6** | 79.1	/ **81.0** | 84.0 / **85.8** |
+| Meta-Llama-3-8b-Instruct | 73.9 / **78.7** | 79.4 / **83.5** | 91.7	/ **92.9** | 73.4	/ **79.6** | 78.3	/ **84.0** | 78.2	/ **79.1** | 64.3	/ **69.4** | 56.2	/ **63.2** | 74.4 / **78.8** |
+| Phi3-mini-128k-Instruct  | 73.4 / **74.8** | 85.7	/ **88.0** | 95.4	/ **96.0** | 82.8	/ **87.8** | 80.4	/ **84.2** | **74.7**	/ 73.9 | 59.3	/ **64.6** | 67.3	/ **72.9** | 77.4 / **80.5** |
+You can also download the official paper results at the following [Google Drive Link](https://drive.google.com/file/d/1l7bY-TkqnmVQn5M5ynQfT-0upMcRlMnT/view?usp=drive_link).
+## Cite this work
+If you use any part of this work, please consider citing the paper as follows:
+```bibtex
+@inproceedings{molfese-etal-2024-zebra,
+    title = "ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering",
+    author = "Molfese, Francesco Maria  and
+      Conia, Simone  and
+      Orlando, Riccardo  and
+      Navigli, Roberto",
+    editor = "",
+    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2024",
+    address = "Miami",
+    publisher = "Association for Computational Linguistics",
+    url = "",
+    doi = "",
+    pages = "",
+    abstract = "",
+}
+```
+## 🪪 License
+The data and software are licensed under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+## Acknowledgements
+[Future AI Research](https://future-ai-research.it/) and CREATIVE (CRoss-modalunderstanding and gEnerATIon of Visual and tExtual content) for supporting this work.