  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).
