# ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice

arXiv: https://arxiv.org/abs/2408.07137 · HTML: https://mlsanity.com/paper/2408.07137/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [SaulLM-7B: A pioneering Large Language Model for Law](https://mlsanity.com/paper/2403.03883/) (2024)
- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services](https://mlsanity.com/paper/2309.11325/) (2023)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3](https://mlsanity.com/paper/2302.05729/) (2023)
- [Explaining Legal Concepts with Augmented Large Language Models (GPT-4)](https://mlsanity.com/paper/2306.09525/) (2023)
- [Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task](https://mlsanity.com/paper/2310.06504/) (2023)
- [CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding](https://mlsanity.com/paper/2305.05393/) (2023)

## Built on by (most cited first, 5 of 5)

- [Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers](https://mlsanity.com/paper/2606.02776/) (2026)
- [When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval](https://mlsanity.com/paper/2606.17220/) (2026)
- [ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval](https://mlsanity.com/paper/2505.08130/) (2025)
- [Legal Experts Disagree With Rationale Extraction Techniques for Explaining ECtHR Case Outcome Classification](https://mlsanity.com/paper/2601.12419/) (2026)
- [LawCompass: Navigating from Legal QA to Multi-Agent Deep Research with Grounded Evidence](https://mlsanity.com/paper/2610.01027/) (2026)

## Abstract (arXiv)

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 Empowering bf LLMs for interpretable, accurate, and informative bf Legal bf Advice. 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.