A Survey on Legal Question Answering Systems
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Abstract (arXiv)
Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal domain, the research community agrees that a system allowing to generate automatic responses to legal questions could substantially impact many practical implications in daily activities. The degree of usefulness is such that even a semi-automatic solution could significantly help to reduce the workload to be faced. This is mainly because a Question Answering system could be able to automatically process a massive amount of legal resources to answer a question or doubt in seconds, which means that it could save resources in the form of effort, money, and time to many professionals in the legal sector. In this work, we quantitatively and qualitatively survey the solutions that currently exist to meet this challenge.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 125 references that are in the map. First posted on arXiv 12 Oct 2021.
Builds on
18 of 125 references.
References · 18
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6 more
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Built on by
Citing papers per year: 2023: 5; 2024: 1; 2025: 2; 2026: 2.
10 papers.
- 2023AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languagescited by 23
- 2023Product Question Answering in E-Commerce: A Surveycited by 10
- 2023Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Modelscited by 10
- 2025Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extractioncited by 2
- 2023RISC: Generating Realistic Synthetic Bilingual Insurance Contractcited by 1
- 2025AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statementscited by 1
- 2023Exploring the State of the Art in Legal QA Systemscited by 0
- 2024Efficient Learning Content Retrieval with Knowledge Injectioncited by 0
- 2026ViHERMES: A Graph-Grounded Multihop Question Answering Benchmark and System for Vietnamese Healthcare Regulationscited by 0
- 2026AI-Assisted Moot Courts: Simulating Justice-Specific Questioning in Oral Argumentscited by 0
- 2026AI-Assisted Moot Courts: Simulating Justice-Specific Questioning in Oral Arguments5 Mar 2026
- 2026ViHERMES: A Graph-Grounded Multihop Question Answering Benchmark and System for Vietnamese Healthcare Regulations7 Feb 2026
- 2025Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction9 Aug 2025
- 2025AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements10 Feb 2025
- 2024Efficient Learning Content Retrieval with Knowledge Injection28 Nov 2024
- 2023Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Models26 Sep 2023
- 2023AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages11 May 2023
- 2023Exploring the State of the Art in Legal QA Systems13 Apr 2023
- 2023RISC: Generating Realistic Synthetic Bilingual Insurance Contract9 Apr 2023
- 2023Product Question Answering in E-Commerce: A Survey16 Feb 2023