se7enese@ALQAC 2024: Experimenting New Released Models in Legal Document Retrieval and Question Answering

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Hoang-Bao Le, Liting Zhou, Cathal Gurrin

Published: 2024, Last Modified: 15 Jan 2026KSE 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This paper describes the se7enese team's efforts in the Automated Legal Question Answering Competition (ALQAC) 2024. In this competition, there are two tasks related to law documents: Document Retrieval and Question Answering. In the first task, we implemented three versions of BM25 - a wellknown techniquel for ranking documents. In the second task, we utilise the latest version of the Large Language Model (LLM) LLaMA - LLaMA 3. Our methods introduce the potential of new models in the competition. The code can be found at https://github.com/baohl00/alqac24.