Evaluation Ethics of LLMs in Legal Domain
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Abstract (arXiv)
In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains. However, their universal competence in addressing challenges specific to specialized fields such as law remains a subject of scrutiny. The incorporation of legal ethics into the model has been overlooked by researchers. We asserts that rigorous ethic evaluation is essential to ensure the effective integration of large language models in legal domains, emphasizing the need to assess domain-specific proficiency and domain-specific ethic. To address this, we propose a novelty evaluation methodology, utilizing authentic legal cases to evaluate the fundamental language abilities, specialized legal knowledge and legal robustness of large language models (LLMs). The findings from our comprehensive evaluation contribute significantly to the academic discourse surrounding the suitability and performance of large language models in legal domains.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 17 Mar 2024.
Builds on
4 of 18 references.
References · 4
- 2023AGIEval: A Human-Centric Benchmark for Evaluating Foundation Modelscited by 701
- 2023Baichuan 2: Open Large-scale Language Modelscited by 664
- 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
- 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 119
Built on by
Citing papers per year: 2024: 2; 2025: 3; 2026: 3.
8 papers.
- 2024CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judgescited by 32
- 2025The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMscited by 24
- 2025LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversationcited by 19
- 2025Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responsescited by 7
- 2026LLM Agents in Law: Taxonomy, Applications, and Challengescited by 5
- 2024Logicbreaks: A Framework for Understanding Subversion of Rule-based Inferencecited by 3
- 2026LegalOne: A Family of Foundation Models for Reliable Legal Reasoningcited by 3
- 2026Ethics Testing: Proactive Identification of Generative AI System Harmscited by 0
- 2026Ethics Testing: Proactive Identification of Generative AI System Harms23 Apr 2026
- 2026LegalOne: A Family of Foundation Models for Reliable Legal Reasoning31 Jan 2026
- 2026LLM Agents in Law: Taxonomy, Applications, and Challenges8 Jan 2026
- 2025Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses19 Sep 2025
- 2025The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs6 Jun 2025
- 2025LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation28 Feb 2025
- 2024CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges20 Oct 2024
- 2024Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference21 Jun 2024