Summary of Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation Of Vietnamese Large Language Models, by Sang T. Truong et al.
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models
by Sang T. Truong, Duc Q. Nguyen, Toan Nguyen, Dong D. Le, Nhi N. Truong, Tho Quan, Sanmi Koyejo
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in large language models (LLMs) have demonstrated their critical role in AI development. Despite extensive pre-training on multilingual datasets, existing open-sourced LLMs show limited effectiveness when processing Vietnamese text. This limitation is compounded by the lack of standardized benchmark datasets and metrics for evaluating Vietnamese LLM performance. To address these issues, we fine-tuned LLMs specifically for Vietnamese and developed a comprehensive evaluation framework incorporating 10 common tasks and 31 metrics. Our results reveal that fine-tuned LLMs exhibit enhanced comprehension and generative capabilities in Vietnamese. Furthermore, our analysis highlights the significance of meticulous fine-tuning with high-quality datasets in improving LLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer models better at understanding and generating Vietnamese text. Right now, these models aren’t very good at this because they weren’t trained on enough Vietnamese data. To fix this problem, the researchers took existing language models and trained them specifically for Vietnamese. They also created a set of tests to measure how well these models do. The results show that these models are much better at understanding and generating Vietnamese text when they’re trained just for Vietnamese. This is important because it can help us make more accurate translations and understand more about the Vietnamese language. |
Keywords
» Artificial intelligence » Fine tuning
