Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910459589820416 |
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| author | Truong, Sang T. Nguyen, Duc Q. Nguyen, Toan Le, Dong D. Truong, Nhi N. Quan, Tho Koyejo, Sanmi |
| author_facet | Truong, Sang T. Nguyen, Duc Q. Nguyen, Toan Le, Dong D. Truong, Nhi N. Quan, Tho Koyejo, Sanmi |
| contents | Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence. However, despite extensive pretraining on multilingual datasets, available open-sourced LLMs exhibit limited effectiveness in processing Vietnamese. The challenge is exacerbated by the absence of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation. To mitigate these issues, we have finetuned LLMs specifically for Vietnamese and developed a comprehensive evaluation framework encompassing 10 common tasks and 31 metrics. Our evaluation results reveal that the fine-tuned LLMs exhibit enhanced comprehension and generative capabilities in Vietnamese. Moreover, our analysis indicates that models with more parameters can introduce more biases and uncalibrated outputs and the key factor influencing LLM performance is the quality of the training or fine-tuning datasets. These insights underscore the significance of meticulous fine-tuning with high-quality datasets in enhancing LLM performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02715 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models Truong, Sang T. Nguyen, Duc Q. Nguyen, Toan Le, Dong D. Truong, Nhi N. Quan, Tho Koyejo, Sanmi Computation and Language Artificial Intelligence 68T50 Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence. However, despite extensive pretraining on multilingual datasets, available open-sourced LLMs exhibit limited effectiveness in processing Vietnamese. The challenge is exacerbated by the absence of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation. To mitigate these issues, we have finetuned LLMs specifically for Vietnamese and developed a comprehensive evaluation framework encompassing 10 common tasks and 31 metrics. Our evaluation results reveal that the fine-tuned LLMs exhibit enhanced comprehension and generative capabilities in Vietnamese. Moreover, our analysis indicates that models with more parameters can introduce more biases and uncalibrated outputs and the key factor influencing LLM performance is the quality of the training or fine-tuning datasets. These insights underscore the significance of meticulous fine-tuning with high-quality datasets in enhancing LLM performance. |
| title | Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models |
| topic | Computation and Language Artificial Intelligence 68T50 |
| url | https://arxiv.org/abs/2403.02715 |