--- 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: 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 - 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 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: 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-* - config_name: Translation data_files: - split: test path: Translation/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.