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<p>Large language models (LLMs), trained on extensive datasets, demonstrate their exceptional ability to handle textual data to yield desired outputs solely through prompts and demonstrations, eliminating the need for extensive fine-tuning. Despite their potential to change lives, integrating LLMs into the legal domain poses challenges, necessitating high-level reasoning, efficient understanding of complex linguistic structures, and faithful reflection of legal precedents. Tailoring prompting paradigm LLMs through reasoning techniques to address legal nuances enhances their precision and relevance in generating legal text. Despite advancements in language processing, challenges persist, particularly the lack of domain-specific knowledge in LLMs. Approaches such as integrating external information retrieval systems and fine-tuning with domain-specific data aim to mitigate this gap. This research provides a comprehensive exploration of adaptive strategies aimed at increasing the reasoning capabilities of LLMs within the legal domain. The paper delves into various prompt engineering methodologies, approaches to incorporate external knowledge, and frameworks for evaluating LLM responses, with the aim of inspiring further research in prompt engineering specifically tailored for legal applications while addressing prevailing research challenges.</p>

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Advancing prompt-based language models in the legal domain: adaptive strategies and research challenges

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • Reshma Sheik,
  • Sneha Ann Reji,
  • A. Sharon,
  • M. Avisha Rai,
  • S. Jaya Nirmala

摘要

Large language models (LLMs), trained on extensive datasets, demonstrate their exceptional ability to handle textual data to yield desired outputs solely through prompts and demonstrations, eliminating the need for extensive fine-tuning. Despite their potential to change lives, integrating LLMs into the legal domain poses challenges, necessitating high-level reasoning, efficient understanding of complex linguistic structures, and faithful reflection of legal precedents. Tailoring prompting paradigm LLMs through reasoning techniques to address legal nuances enhances their precision and relevance in generating legal text. Despite advancements in language processing, challenges persist, particularly the lack of domain-specific knowledge in LLMs. Approaches such as integrating external information retrieval systems and fine-tuning with domain-specific data aim to mitigate this gap. This research provides a comprehensive exploration of adaptive strategies aimed at increasing the reasoning capabilities of LLMs within the legal domain. The paper delves into various prompt engineering methodologies, approaches to incorporate external knowledge, and frameworks for evaluating LLM responses, with the aim of inspiring further research in prompt engineering specifically tailored for legal applications while addressing prevailing research challenges.