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.

翻译：尽管结合法律条文检索组件的法律大型语言模型在法律咨询方面表现出卓越性能，但其提供的建议仍存在错误或无依据的情况。为缓解这些问题，我们提出{\bf ELLA}工具，旨在{\bf E}mpowering {\bf L}LMs以提供可解释、准确且信息丰富的{\bf L}egal {\bf A}dvice。ELLA通过计算法律条文与LLM响应之间的相似度，可视化呈现二者的关联性，为用户提供直观的响应法律依据。此外，基于用户查询，ELLA会检索相关法律条文并向用户展示。用户可交互式选择法律条文供LLM生成更精准的响应。ELLA还检索相关法律案例供用户参考。我们的用户研究表明：呈现响应的法律依据有助于用户更好地理解；当用户介入选择法律条文时，LLM响应的准确性亦得到提升；提供相关法律案例还能帮助个体获取全面信息。