ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice
This addresses the need for more reliable and interpretable legal advice for users, though it is incremental as it builds on existing retrieval methods.
The paper tackles the problem of incorrect or baseless legal advice from LLMs by proposing ELLA, a tool that visually correlates legal articles with responses and allows user interaction to improve accuracy, with user studies showing enhanced understanding and accuracy.
Despite remarkable performance in legal consultation exhibited by legal Large Language Models(LLMs) combined with legal article retrieval components, there are still cases when the advice given is incorrect or baseless. To alleviate these problems, we propose {\bf ELLA}, a tool for {\bf E}mpowering {\bf L}LMs for interpretable, accurate, and informative {\bf L}egal {\bf A}dvice. ELLA visually presents the correlation between legal articles and LLM's response by calculating their similarities, providing users with an intuitive legal basis for the responses. Besides, based on the users' queries, ELLA retrieves relevant legal articles and displays them to users. Users can interactively select legal articles for LLM to generate more accurate responses. ELLA also retrieves relevant legal cases for user reference. Our user study shows that presenting the legal basis for the response helps users understand better. The accuracy of LLM's responses also improves when users intervene in selecting legal articles for LLM. Providing relevant legal cases also aids individuals in obtaining comprehensive information.