Summary of Villm-eval: a Comprehensive Evaluation Suite For Vietnamese Large Language Models, by Trong-hieu Nguyen et al.
ViLLM-Eval: A Comprehensive Evaluation Suite for Vietnamese Large Language Models
by Trong-Hieu Nguyen, Anh-Cuong Le, Viet-Cuong Nguyen
First submitted to arxiv on: 17 Apr 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 This research introduces ViLLM-Eval, an evaluation suite designed to assess the capabilities of foundation models in a Vietnamese context. The suite consists of multiple-choice questions and predict next word tasks across various difficulty levels and disciplines, including humanities, science, and engineering. The evaluation revealed that even top-performing large language models have significant room for improvement in understanding and responding to Vietnamese language tasks. ViLLM-Eval is expected to identify key strengths and weaknesses of foundation models, promoting their development and enhancing their performance for Vietnamese users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to test how well computers understand and can use the Vietnamese language. It’s like giving them a series of questions and tasks to see how well they do. The tests cover different levels of difficulty and topics like science, history, and technology. The results showed that even the best computer models are not perfect at understanding Vietnamese yet. This new way of testing will help improve these models so people can use them better for things like language translation and answering questions. |
Keywords
» Artificial intelligence » Translation
