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Kalahi: A handcrafted, grassroots cultural LLM evaluation suite for Filipino

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arxiv 2409.15380 v4 pith:PWFNFB3B submitted 2024-09-20 cs.CL cs.AI

classification cs.CLcs.AI
keywords filipinokalahillmsculturalevaluationhandcraftedlanguagemodel
verification ladder T0 review T1 audit T2 compute T3 formal
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Multilingual large language models (LLMs) today may not necessarily provide culturally appropriate and relevant responses to its Filipino users. We introduce Kalahi, a cultural LLM evaluation suite collaboratively created by native Filipino speakers. It is composed of 150 high-quality, handcrafted and nuanced prompts that test LLMs for generations that are relevant to shared Filipino cultural knowledge and values. Strong LLM performance in Kalahi indicates a model's ability to generate responses similar to what an average Filipino would say or do in a given situation. We conducted experiments on LLMs with multilingual and Filipino language support. Results show that Kalahi, while trivial for Filipinos, is challenging for LLMs, with the best model answering only 46.0% of the questions correctly compared to native Filipino performance of 89.10%. Thus, Kalahi can be used to accurately and reliably evaluate Filipino cultural representation in LLMs.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness

    cs.CY 2025-02 conditional novelty 6.0 of 10

    The paper argues that LLMs should be evaluated and built for meta-cultural competence rather than static knowledge of specific cultures, and gives a first, illustrative measurement of one component.

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