Optimizing Answer Generator in Vietnamese Legal Question Answering Systems Using Language Models

The development of large language models (LLMs) such as ChatGPT and Gemini has led to impressive advancements in question answering (QA) systems. However, they often rely on generic knowledge from the internet, resulting in hallucinated answers when applied to domain-specific QA tasks. Furthermore, their operational dependence on powerful GPUs poses challenges for practical software deployment. Building a QA systems for low-resource languages like Vietnamese is even more challenging due to the scarcity of labeled data and limited pre-trained language models. In this study, we aim at constructing a Vietnamese legal QA system using a retrieval-augmented generation approach to reduce incorrect outputs. Our focus is on improving answer generation accuracy by training small-scale LLMs suitable for real-world deployment. Our contributions are: (i) constructing Vietnamese legal provisions and QA datasets for training the system; and (ii) proposing methods to fine-tune language models with QA capabilities in the legal domain. Experimental results demonstrate that it is possible to train an LLM with fewer computational resources and a smaller dataset while maintaining effectiveness. Our findings highlight that designing an efficient training and fine-tuning strategy is crucial for overcoming these challenges, particularly in the context of Vietnamese legal question-answering tasks.