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Evaluation Ethics of LLMs in Legal Domain

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arxiv 2403.11152 v1 pith:TJ7CWX33 submitted 2024-03-17 cs.CL cs.AI

Evaluation Ethics of LLMs in Legal Domain

classification cs.CL cs.AI
keywords legallanguageevaluationlargemodelsdomainsdomain-specificethic
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Ethics Testing: Proactive Identification of Generative AI System Harms

    cs.SE 2026-04 unverdicted novelty 6.0

    Ethics testing is introduced as a systematic approach to generate tests that identify software harms induced by unethical behavior in generative AI outputs.

  2. A Survey on LLM-as-a-Judge

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    A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.