Summary of Representing the Under-represented: Cultural and Core Capability Benchmarks For Developing Thai Large Language Models, by Dahyun Kim et al.
Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models
by Dahyun Kim, Sukyung Lee, Yungi Kim, Attapol Rutherford, Chanjun Park
First submitted to arxiv on: 7 Oct 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 rapid advancement of large language models (LLMs) has led to the need for robust evaluation frameworks that assess their core capabilities, such as reasoning, knowledge, and commonsense. A widely-used benchmark suite is the H6 benchmark, but it primarily caters to the English language, leaving a gap for under-represented languages like Thai. Developing LLMs for Thai requires enhancing cultural understanding alongside core capabilities. To address this dual challenge, the authors propose two key benchmarks: Thai-H6 and Thai Cultural and Linguistic Intelligence Benchmark (ThaiCLI). A comprehensive analysis is provided through thorough evaluation of various LLMs with multi-lingual capabilities, highlighting how these benchmarks contribute to Thai LLM development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a problem in language models. Language models are getting better and better, but they mostly work for one language: English. This leaves out many other languages like Thai. To fix this, the researchers propose two new ways to test language models for Thai. They tested different models that can understand multiple languages and compared how well they did on these tests. The results show how helpful these new tests are for making better language models for Thai. |
