FilBench evaluates LLMs for Filipino languages (Tagalog, Cebuano) across translation and cultural tasks

AI Impact Summary

FilBench provides a systematic evaluation of LLMs for Philippine languages (Tagalog, Filipino, Cebuano) across Cultural Knowledge, Classical NLP, Reading Comprehension, and Generation. It benchmarks 20+ models using translation-focused tasks and retention of culturally specific knowledge, built on HuggingFace's Lighteval framework. Findings show SEA-specific open-weight models offer strong cost-efficiency and comparable scores to larger models at scale, but GPT-4o still leads on translation fidelity and generation quality; translation tasks remain a pain point with risk of verbose output or language leakage. The results imply you should run FilBench on your target models to guide procurement, fine-tuning, and data curation decisions for Filipino tasks.