FilBench: Can LLMs Understand and Generate Filipino?
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Abstract (arXiv)
Despite the impressive performance of LLMs on English-based tasks, little is known about their capabilities in specific languages such as Filipino. In this work, we address this gap by introducing FilBench, a Filipino-centric benchmark designed to evaluate LLMs across a diverse set of tasks and capabilities in Filipino, Tagalog, and Cebuano. We carefully curate the tasks in FilBench to reflect the priorities and trends of NLP research in the Philippines such as Cultural Knowledge, Classical NLP, Reading Comprehension, and Generation. By evaluating 27 state-of-the-art LLMs on FilBench, we find that several LLMs suffer from reading comprehension and translation capabilities. Our results indicate that FilBench is challenging, with the best model, GPT-4o, achieving only a score of 72.23%. Moreover, we also find that models trained specifically for Southeast Asian languages tend to underperform on FilBench, with the highest-performing model, SEA-LION v3 70B, achieving only a score of 61.07%. Our work demonstrates the value of curating language-specific LLM benchmarks to aid in driving progress on Filipino NLP and increasing the inclusion of Philippine languages in LLM development.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 34 of its 72 references that are in the map. First posted on arXiv 5 Aug 2025.
Builds on
34 of 72 references.
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22 more
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Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languagescited by 3
- 2025KatotohananQA: Evaluating Truthfulness of Large Language Models in Filipinocited by 0
- 2026Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speechcited by 0
- 2026Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech25 May 2026
- 2025KatotohananQA: Evaluating Truthfulness of Large Language Models in Filipino7 Sep 2025
- 2025Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages2 Sep 2025