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This paper highlights the limitations of applying large-scale language models (LLMs) to the legal field and the challenges of processing legal text in low-resource languages such as Vietnamese. To address the resource shortage in the Vietnamese legal field, we present VLQA, a high-quality Vietnamese legal question-answering dataset, and evaluate its effectiveness in legal information retrieval and question-answering tasks using state-of-the-art models. We emphasize that the capabilities of LLMs tend to be overestimated and that there is still a long way to go before fully automating legal work.
Takeaways, Limitations
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Takeaways: By providing a high-quality dataset, VLQA, to address the resource-limited problem in the Vietnamese legal field, this study makes a significant contribution to Vietnamese legal text processing research. It also highlights the practical limitations of LLM-based legal text processing research and the diversity of multilingual legal systems.
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Limitations: Further validation of the scale and quality of the VLQA dataset may be necessary. The Vietnamese legal issues addressed in this paper may apply to other low-resource languages, but the unique characteristics of each language must be taken into account. There is a lack of discussion on the ethical and social implications of LLM-based legal automation.
