Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese
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Abstract (arXiv)
Large Language Models (LLMs) are increasingly being used to generate synthetic data for training and evaluating models. However, it is unclear whether they can generate a good quality of question answering (QA) dataset that incorporates knowledge and cultural nuance embedded in a language, especially for low-resource languages. In this study, we investigate the effectiveness of using LLMs in generating culturally relevant commonsense QA datasets for Indonesian and Sundanese languages. To do so, we create datasets for these languages using various methods involving both LLMs and human annotators, resulting in ~4.5K questions per language (~9K in total), making our dataset the largest of its kind. Our experiments show that automatic data adaptation from an existing English dataset is less effective for Sundanese. Interestingly, using the direct generation method on the target language, GPT-4 Turbo can generate questions with adequate general knowledge in both languages, albeit not as culturally 'deep' as humans. We also observe a higher occurrence of fluency errors in the Sundanese dataset, highlighting the discrepancy between medium- and lower-resource languages.
Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 27 Feb 2024.
Builds on
17 of 51 references.
References · 17
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- 2022NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languagescited by 67
- 2022One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesiacited by 65
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Citing papers per year: 2024: 10; 2025: 18; 2026: 8.
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- 2025Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMscited by 12
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- 2025RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Setscited by 1
- 2026Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification22 Jan 2026
- 2025RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets7 Oct 2025
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- 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 101
- 2024CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologiescited by 98
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- 2026No Shortcuts to Culture: Indonesian Multi-hop Question Answering for Complex Cultural Understanding3 Feb 2026
- 2026Injecting Knowledge from Social Science Journals to Improve Indonesian Cultural Understanding by LLMs19 Jan 2026
- 2026CALM: Culturally Self-Aware Language Models7 Jan 2026
- 2026To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs6 Jan 2026
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- 2025ScIRGen: Synthesize Realistic and Large-Scale RAG Dataset for Scientific Research9 Jun 2025
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