Summary of Kalahi: a Handcrafted, Grassroots Cultural Llm Evaluation Suite For Filipino, by Jann Railey Montalan et al.
Kalahi: A handcrafted, grassroots cultural LLM evaluation suite for Filipino
by Jann Railey Montalan, Jian Gang Ngui, Wei Qi Leong, Yosephine Susanto, Hamsawardhini Rengarajan, Alham Fikri Aji, William Chandra Tjhi
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Kalahi, a novel evaluation suite for multilingual large language models (LLMs) that assess their ability to generate culturally appropriate responses relevant to Filipino culture. Kalahi consists of 150 carefully crafted prompts that test LLMs’ understanding of shared cultural knowledge and values. The authors evaluate popular LLMs with multilingual support and find that even the best-performing model answers only 46% of questions correctly, compared to native Filipinos who achieve a score of 89.10%. This highlights the need for accurate evaluation methods like Kalahi to ensure that LLMs can effectively represent Filipino culture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure language models are good at understanding and responding in a way that’s relevant to people from the Philippines. The authors created a special test called Kalahi, which has 150 questions that check if language models are saying things that Filipinos would say or do. They tested some popular language models and found that even the best one only got about half of the answers right, while native Filipinos got most of them correct. This shows how important it is to have a good test like Kalahi to make sure language models can represent Filipino culture well. |
