Legal Prompting: Teaching a Language Model to Think Like a Lawyer
(2212.01326)Abstract
Large language models that are capable of zero or few-shot prompting approaches have given rise to the new research area of prompt engineering. Recent advances showed that for example Chain-of-Thought (CoT) prompts can improve arithmetic or common sense tasks significantly. We explore how such approaches fare with legal reasoning tasks and take the COLIEE entailment task based on the Japanese Bar exam for testing zero-shot/few-shot and fine-tuning approaches. Our findings show that while CoT prompting and fine-tuning with explanations approaches show improvements, the best results are produced by prompts that are derived from specific legal reasoning techniques such as IRAC (Issue, Rule, Application, Conclusion). Based on our experiments we improve the 2021 best result from 0.7037 accuracy to 0.8148 accuracy and beat the 2022 best system of 0.6789 accuracy with an accuracy of 0.7431.
Overview
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The study explore how different prompting and fine-tuning strategies can improve LLMs' performance on the COLIEE entailment task based on Japanese Bar Exam questions.
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It experiments with zero-shot, few-shot, fine-tuning with explanations, and legal reasoning prompts using TRRAC and IRREAC methodologies to assess their impact on LLMs' ability to understand legal texts.
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Legal reasoning prompts, especially with structures like TRRAC, significantly enhanced LLMs' accuracy, outperforming standard zero-shot and few-shot approaches.
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The research indicates a potential for further optimization and suggests exploring advanced prompting strategies and the application of LLMs in various legal systems and other specialized domains.
Exploring Legal Reasoning in Language Models: An Evaluation on the COLIEE Entailment Task
Introduction
Recent advancements in NLP have opened up intriguing avenues for applying LLMs to domain-specific tasks such as legal reasoning. This particular study sets out to investigate how various prompting and fine-tuning strategies can enhance the performance of LLMs on the Complex Legal Information Extraction/Entailment (COLIEE) entailment task, a challenge based on questions from the Japanese Bar Exam.
Methodology
The research utilizes the COLIEE competition format, emphasizing tasks that involve discerning the veracity of a legal hypothesis given a set of premises extracted from Japanese statutes. The analysis spans zero-shot, few-shot, fine-tuning with explanations, and legal reasoning prompt approaches to assess their performance across different versions of the COLIEE dataset (2021 and 2022).
- Zero-shot (ZS) and few-shot (FS) techniques were tested using a variety of prompts to inspect their innate capability in deciphering legal entailments without prior domain-specific training.
- The study further experimented with fine-tuning approaches, incorporating both binary answers and explanatory responses to gauge improvements in predictive accuracy.
- In a novel approach, the paper also explored legal reasoning prompts based on common legal reasoning methodologies (e.g., IRAC method), hypothesizing that providing structured reasoning frameworks could significantly aid the model's performance.
Results
A key finding from this effort is the remarkable effectiveness of legal reasoning prompts, with certain methodologies (TRRAC and IRREAC) notably surpassing standard zero-shot and few-shot approaches. For the 2021 dataset, a tailored legal reasoning approach (TRRAC) achieved an accuracy of 0.8148, improving upon the best system from 2021 by 15.79%. Meanwhile, the 8-shot approach demonstrated robustness over both 2021 and 2022 datasets, outperforming other strategies in the latter year.
The results also highlighted an inconsistency with zero-shot reasoning across different datasets, suggesting that while highly effective in certain instances, it may require additional refinement for general applicability across variable legal entailment tasks. Interestingly, fine-tuning with pseudo-explanations did not yield improvements over other methods, indicating that the quality of explanations plays a crucial role in the model's learning process.
Implications and Future Directions
This study underscores the potential of customized prompting strategies, particularly those imbued with legal reasoning frameworks, in enhancing LLMs' capability to tackle complex domain-specific tasks. The substantial improvements observed with legal reasoning prompts hint at the untapped possibilities of aligning LLM prompting strategies with domain-specific thought processes.
The fluctuating performance across different approaches and datasets beckons further investigation into optimizing these strategies for consistency. Future research could explore the amalgamation of these approaches, perhaps through ensemble methods or more sophisticated prompt engineering techniques, to achieve uniformly high performance across varied legal reasoning tasks.
Moreover, while the study centered on legal reasoning within the Japanese jurisdiction, the methodologies discussed herein hold promise for broader applications across different legal systems and perhaps other specialized domains beyond law. These insights pave the way for leveraging LLMs more effectively within specialized fields, moving closer to realizing their potential in understanding and reasoning through complex domain-specific information.
In conclusion, this exploration into legal prompting and reasoning with LLMs not only sets a precedent for future works in legal NLP but also opens up a dialogue on the broader applicability and optimization of LLMs for specialized domain tasks, marking a step forward in the journey towards nuanced, domain-aware artificial intelligence.