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+
+---
+dataset_info:
+- config_name: Causal reasoning
+  features:
+  - name: language
+    dtype: string
+  - name: idx
+    dtype: int64
+  - name: premise
+    dtype: string
+  - name: choice1
+    dtype: string
+  - name: choice2
+    dtype: string
+  - name: question_type
+    dtype: string
+  - name: label
+    dtype: int64
+  splits:
+  - name: test
+    num_bytes: 1286108
+    num_examples: 8395
+  download_size: 662062
+  dataset_size: 1286108
+- config_name: Cultural QA
+  features:
+  - name: language
+    dtype: string
+  - name: context
+    dtype: string
+  - name: option A
+    dtype: string
+  - name: option B
+    dtype: string
+  - name: option C
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 2894281
+    num_examples: 11730
+  download_size: 1709305
+  dataset_size: 2894281
+- config_name: Translation
+  features:
+  - name: Premise ID
+    dtype: int64
+  - name: source_lang
+    dtype: string
+  - name: target_lang
+    dtype: string
+  - name: source
+    dtype: string
+  - name: target
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 1827832
+    num_examples: 5505
+  download_size: 711212
+  dataset_size: 1827832
+- config_name: MCQA
+  features:
+  - name: language
+    dtype: string
+  - name: Pair ID
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: context
+    dtype: string
+  - name: title
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 9366271
+    num_examples: 12995
+  download_size: 3365093
+  dataset_size: 9366271
+- config_name: NLI
+  features:
+  - name: language
+    dtype: string
+  - name: Premise ID
+    dtype: int64
+  - name: Pair ID
+    dtype: int64
+  - name: Premise
+    dtype: string
+  - name: Hypothesis
+    dtype: string
+  - name: Label
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 8411303
+    num_examples: 33258
+  download_size: 2106539
+  dataset_size: 8411303
+- config_name: Open QA
+  features:
+  - name: language
+    dtype: string
+  - name: Pair ID
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 1667688
+    num_examples: 12995
+  download_size: 816653
+  dataset_size: 1667688
+configs:
+- config_name: Causal reasoning
+  data_files:
+  - split: test
+    path: Causal reasoning/test-*
+- config_name: Cultural QA
+  data_files:
+  - split: test
+    path: Cultural QA/test-*
+- config_name: Translation
+  data_files:
+  - split: test
+    path: Translation/test-*
+- config_name: MCQA
+  data_files:
+  - split: test
+    path: MCQA/test-*
+- config_name: NLI
+  data_files:
+  - split: test
+    path: NLI/test-*
+- config_name: Open QA
+  data_files:
+  - split: test
+    path: Open QA/test-*
+---
+# LoraxBench: A Benchmark for Indonesian Local Languages and Registers
+
+## Dataset Summary
+
+LoraxBench is a comprehensive multilingual benchmark focusing on Indonesian and 19 Indonesian local languages, covering 6 diverse NLP tasks. It includes multiple registers for select languages, emphasizing the impact of formal and casual speech on model performance. LoraxBench is professionally translated and validated by natives, and were sourced from Indonesian-originated dataset, Our data is sourced from Indonesian-originated content, thus capturing local nuances better than English-centric data.
+
+LoraxBench fills a critical gap in NLP for Indonesia’s linguistic diversity, where over 700 languages are spoken but few resources exist. Beyond Indonesia, it serves as a valuable resource for modeling challenges common in linguistically rich, resource-scarce regions worldwide.
+
+## Languages
+
+The dataset covers the following 20 languages:
+
+| Language          | ISO Code | Approx. Speakers (millions) | Region             |
+|-------------------|----------|-----------------------------|--------------------|
+| Acehnese          | ace      | 3.7                         | Aceh               |
+| Ambonese Malay    | abs      | 0.2                         | Ambon              |
+| Balinese          | ban      | 4.8                         | Bali               |
+| Banjar            | bjn      | 4.0                         | South Sulawesi     |
+| Batak Toba        | bbc      | 2.5                         | North Sumatra      |
+| Betawi            | bew      | 5.6                         | Jakarta            |
+| Buginese          | bug      | 4.3                         | South Sulawesi     |
+| Gorontalo         | gor      | 1.1                         | Gorontalo          |
+| Iban              | iba      | 0.8                         | West Kalimantan    |
+| Jambi Malay       | jax      | 1.0                         | Jambi              |
+| Javanese          | jv       | 91.0                        | East/Central Java  |
+| Lampung Nyo       | abl      | 1.5                         | Lampung            |
+| Madurese          | mad      | 17.0                        | East Java          |
+| Makasar           | mak      | 1.9                         | Makasar            |
+| Minangkabau       | min      | 8.0                         | West Sumatra       |
+| Musi              | mui      | 3.1                         | South Sumatra      |
+| Ngaju             | nij      | 0.9                         | Central Kalimantan |
+| Sasak             | sas      | 2.6                         | West Nusa Tenggara |
+| Sundanese         | su       | 32.0                        | West Java          |
+| Indonesian        | id       | > 170.0                     | Indonesia          |
+
+## Registers Included
+
+For three languages, LoraxBench includes two distinct registers capturing different levels of formality:
+
+| Language  | Formal Register | Casual Register |
+|-----------|-----------------|-----------------|
+| Javanese  | Krama           | Ngoko           |
+| Sundanese | Lemes           | Loma            |
+| Madurese  | Engghi Ethen    | Enja’Iya        |
+
+Formal registers are used in respectful or formal contexts; casual registers are used among peers and friends, showing significant lexical and stylistic differences.
+
+## Tasks and Data Sources
+
+The following are tasks covered in LoraxBench
+### Reading Comprehension
+
+Answering questions based on Indonesian text passages. This data is translated from the [TyDi QA](https://huggingface.co/datasets/tydiqa) secondary, Indonesian subset.
+
+### Open-Domain Question Answering
+
+Answering questions without access to context passages. This data is derived from the [TyDi QA](https://huggingface.co/datasets/tydiqa) secondary, Indonesian subset.
+
+### Natural Language Inference (NLI)
+
+Determining entailment, contradiction, or neutrality between sentence pairs. This data is translated from the test-expert subset of [IndoNLI](https://huggingface.co/datasets/afaji/indonli), specifically on single-sentence sets.
+
+### Causal Reasoning
+
+Reasoning about cause-effect relations in text. This data is translated from locally-nuanced causal reasoning data, [COPAL-ID](https://huggingface.co/datasets/haryoaw/COPAL). We have filtered some of the entries that are too Jakartan-specific.
+
+### Machine Translation
+
+Translating text to Indonesian. This data is taken from IndoNLI premises, which itself originated from various webpages, news, and articles.
+
+### Cultural Question Answering
+
+Answering culturally relevant questions about Indonesia. We source this from [IndoCulture](https://huggingface.co/datasets/indolem/IndoCulture), with further filtering and clean-up. Specifically, we change some of the distractors that were deemed obviously wrong, fix some typos and writing inconsistencies, as well as remove some trivially easy questions. More on this in the paper.
+
+
+## Personal and Sensitive Information
+
+The corpora contain no personal or sensitive information. Data was sourced and translated with respect to privacy and ethical guidelines.
+
+## Additional Information
+
+- LoraxBench exposes challenges for multilingual models in low-resource and register-variant settings.
+- Benchmark results highlight performance gaps between Indonesian, local languages, and registers.
+
+## Dataset Curators
+
+Google Research
+
+## Licensing Information
+
+This project is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
+
+## Citation Information
+
+Please cite the following papers when using this dataset:
+
+- The main LoraxBench paper
+- Clark et al., 2020. TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages.
+- Mahendra et al., 2021. IndoNLI: A Natural Language Inference Dataset for Indonesian.
+- Wibowo et al., 2024. COPAL-ID: Causal Reasoning in Indonesian.
+- Koto et al., 2024. IndoCulture: Cultural Question Answering in Indonesia.
+- Cahyawijaya et al., 2023. NusaCrowd: Indonesian NLP Dataset Collection.