0x. Y uan at S em E val-2024 Task 5: Enhancing Legal Argument Reasoning with Structured Prompts (2024)

Abstract: The intersection of legal reasoning and Natural Language Processing (NLP) technologies, particularly Large Language Models (LLMs), offers groundbreaking potential for augmenting human capabilities in the legal domain. This paper presents our approach and findings from participating in SemEval-2024 Task 5, focusing on the effect of argument reasoning in civil procedures using legal reasoning prompts. We investigated the impact of structured legal reasoning methodologies, including TREACC, IRAC, IRAAC, and MIRAC, on guiding LLMs to analyze and evaluate legal arguments systematically. Our experimental setup involved crafting specific prompts based on these methodologies to instruct the LLM to dissect and scrutinize legal cases, aiming to discern the cogency of argumentative solutions within a zero-shot learning framework. The performance of our approach, as measured by F1 score and accuracy, demonstrated the efficacy of integrating structured legal reasoning into LLMs for legal analysis. The findings underscore the promise of LLMs, when equipped with legal reasoning prompts, in enhancing their ability to process and reason through complex legal texts, thus contributing to the broader application of AI in legal studies and practice.

## introduction the process of reasoning in legal arguments is a crucial aspect of applying legal knowledge in realworld scenarios. mastery of this skill enables individuals to effectively address legal issues and ascertain the legality of various cases. recently, the field of natural language processing (nlp) has seen significant advancements, leading to the growing trend of utilizing large language models (llms) as tools to augment human capabilities. given the extensive and often complex body of legal knowledge, which can be challenging and time-consuming for the average person to learn, llms present an opportunity to comprehend this information and offer valuable assistance. in light of this, the organizers of semeval-2024 task 5 @xcite have compiled a dataset from the domain of u.s. civil procedure. this dataset includes introductory materials on various cases, a set of questions, potential argumentative solutions, and labels indicating the accuracy of these solutions. this initiative provides a framework for evaluating the effectiveness of llms in the legal arena, thereby contributing to the development of more sophisticated and capable language processing tools for legal applications. in this task, we explored legal reasoning prompts in large language models (llms) @xcite . our focus was on investigating their effectiveness in differentiating argumentative solutions in civil procedure cases. the results show that by guiding an llm to think step-by-step like a lawyer, it significantly outperforms both chain of thought (cot) @xcite reasoning and direct output methods. ## conclusion this study embarked on an exploration of the synergy between large language models (llms) and legal reasoning methodologies to enhance the processing and understanding of legal texts. by integrating structured legal reasoning prompts derived from methodologies such as treacc, irac, iraac, and mirac into the framework of llms, we demonstrated the potential of this approach to improve the models' capacity for legal argument evaluation.