Intelligent Legal Assistant: An Interactive Clarification System for Legal Question Answering
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Abstract (arXiv)
The rise of large language models has opened new avenues for users seeking legal advice. However, users often lack professional legal knowledge, which can lead to questions that omit critical information. This deficiency makes it challenging for traditional legal question-answering systems to accurately identify users' actual needs, often resulting in imprecise or generalized advice. In this work, we develop a legal question-answering system called Intelligent Legal Assistant, which interacts with users to precisely capture their needs. When a user poses a question, the system requests that the user select their geographical location to pinpoint the applicable laws. It then generates clarifying questions and options based on the key information missing from the user's initial question. This allows the user to select and provide the necessary details. Once all necessary information is provided, the system produces an in-depth legal analysis encompassing three aspects: overall conclusion, jurisprudential analysis, and resolution suggestions.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 11 Feb 2025.
Builds on
3 of 4 references.
References · 3
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2024Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learningcited by 12
Built on by
Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligencecited by 4
- 2026InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioningcited by 3
- 2025Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domainscited by 2
- 2025Universal Legal Article Prediction via Tight Collaboration between Supervised Classification Model and LLMcited by 2
- 2026"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborationscited by 1
- 2026"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations16 Apr 2026
- 2026InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning6 Mar 2026
- 2025LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence4 Dec 2025
- 2025Universal Legal Article Prediction via Tight Collaboration between Supervised Classification Model and LLM26 Sep 2025
- 2025Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains24 Aug 2025