Efficient Finetuning Large Language Models For Vietnamese Chatbot

• URL: http://arxiv.org/abs/2309.04646v1
• Date: Sat, 9 Sep 2023 00:11:53 GMT
• Title: Efficient Finetuning Large Language Models For Vietnamese Chatbot
• Authors: Vu-Thuan Doan, Quoc-Truong Truong, Duc-Vu Nguyen, Vinh-Tiep Nguyen, and Thuy-Ngan Nguyen Luu
• Abstract summary: Large language models (LLMs) have been shown to achieve remarkable performance across a variety of natural language tasks. We leverage large-scale instruction-following datasets from open-source projects, namely Alpaca, GPT4All, and Chat-Doctor. We utilize parameter-efficient tuning through Low-Rank Adaptation (LoRA) on two open LLMs, resulting four models: Bloomz-Chat, Bloomz-Doctor, GPTJ-Chat, GPTJ-Doctor.
• Score: 1.2075778142867704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs), such as GPT-4, PaLM, and LLaMa, have been shown to achieve remarkable performance across a variety of natural language tasks. Recent advancements in instruction tuning bring LLMs with ability in following user's instructions and producing human-like responses. However, the high costs associated with training and implementing LLMs pose challenges to academic research. Furthermore, the availability of pretrained LLMs and instruction-tune datasets for Vietnamese language is limited. To tackle these concerns, we leverage large-scale instruction-following datasets from open-source projects, namely Alpaca, GPT4All, and Chat-Doctor, which cover general domain and specific medical domain. To the best of our knowledge, these are the first instructional dataset for Vietnamese. Subsequently, we utilize parameter-efficient tuning through Low-Rank Adaptation (LoRA) on two open LLMs: Bloomz (Multilingual) and GPTJ-6B (Vietnamese), resulting four models: Bloomz-Chat, Bloomz-Doctor, GPTJ-Chat, GPTJ-Doctor.Finally, we assess the effectiveness of our methodology on a per-sample basis, taking into consideration the helpfulness, relevance, accuracy, level of detail in their responses. This evaluation process entails the utilization of GPT-4 as an automated scoring mechanism. Despite utilizing a low-cost setup, our method demonstrates about 20-30\% improvement over the original models in our evaluation tasks.

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