ViLLM-Eval: A Comprehensive Evaluation Suite for Vietnamese Large Language Models
This addresses the problem of assessing and improving large language models for Vietnamese users, though it is incremental as it adapts existing evaluation methods to a new language context.
The authors tackled the lack of benchmarks for evaluating Vietnamese large language models by introducing ViLLM-Eval, a comprehensive evaluation suite, and found that even top models have significant room for improvement in Vietnamese language tasks.
The rapid advancement of large language models (LLMs) necessitates the development of new benchmarks to accurately assess their capabilities. To address this need for Vietnamese, this work aims to introduce ViLLM-Eval, the comprehensive evaluation suite designed to measure the advanced knowledge and reasoning abilities of foundation models within a Vietnamese context. ViLLM-Eval consists of multiple-choice questions and predict next word tasks spanning various difficulty levels and diverse disciplines, ranging from humanities to science and engineering. A thorough evaluation of the most advanced LLMs on ViLLM-Eval revealed that even the best performing models have significant room for improvement in understanding and responding to Vietnamese language tasks. ViLLM-Eval is believed to be instrumental in identifying key strengths and weaknesses of foundation models, ultimately promoting their development and enhancing their performance for Vietnamese users. This paper provides a thorough overview of ViLLM-Eval as part of the Vietnamese Large Language Model shared task, held within the 10th International Workshop on Vietnamese Language and Speech Processing (VLSP 2023).