2024 AnsweringQuestionsinStagesPromp

• (Roegiest & Chitta, 2024) ⇒ Adam Roegiest, and Radha Chitta. (2024). “Answering Questions in Stages: Prompt Chaining for Contract QA.” doi:10.48550/arXiv.2410.12840

Subject Headings: Two-Stage Prompt Chaining, LLM Performance Evaluation, Force Majeure Clause,Exact Match Accuracy.

Notes

• The paper demonstrates that two-stage prompt chaining can significantly improve LLM performance on complex legal questions compared to single-stage prompts.
• The paper reveals that including answer options in the first stage of prompt chaining further enhances the model's performance.
• The paper identifies limitations in the approach when dealing with high linguistic variation, particularly in force majeure clauses.
• The paper observes that summary formats generated by LLMs vary based on question type, with expository styles for reasoning questions and list-like formats for enumeration questions.
• The paper highlights the importance of tailoring summaries to specific questions and desired structured outputs to filter out irrelevant information.
• The paper underscores the sensitivity of prompt engineering to specific wording, noting that small changes can lead to significant variations in output.
• The paper suggests that the approach has potential applications in high-stakes legal environments such as mergers and acquisitions, contract drafting, and risk mitigation.
• The paper identifies the need for further research into pre-training or fine-tuning LLMs specifically on legal contracts and legal questions to enhance performance.
• The paper proposes the development of more sophisticated prompt templates that incorporate clause definitions to improve accuracy.
• The paper emphasizes the importance of addressing hallucination in legal systems as a critical area for future improvement in AI-assisted legal analysis.
• The paper demonstrates the value of using multiple evaluation metrics (precision, recall, and exact match accuracy) to provide a comprehensive assessment of LLM performance in legal question-answering tasks.

Cited By

• http://scholar.google.com/scholar?q=%222024%22+Answering+Questions+in+Stages%3A+Prompt+Chaining+for+Contract+QA

Quotes

Abstract

Finding answers to legal questions about clauses in contracts is an important form of analysis in many legal workflows (e.g., understanding market trends, due diligence, risk mitigation) but more important is being able to do this at scale. Prior work showed that it is possible to use large language models with simple zero-shot prompts to generate structured answers to questions, which can later be incorporated into legal workflows. Such prompts, while effective on simple and straightforward clauses, fail to perform when the clauses are long and contain information not relevant to the question. In this paper, we propose two-stage prompt chaining to produce structured answers to multiple-choice and multiple-select questions and show that they are more effective than simple prompts on more nuanced legal text. We analyze situations where this technique works well and areas where further refinement is needed, especially when the underlying linguistic variations are more than can be captured by simply specifying possible answers. Finally, we discuss future research that seeks to refine this work by improving stage one results by making them more question-specific.

1. Problem Statement and Motivation

• SUMMARY: The research addresses a critical challenge in legal tech: using LLMs to accurately answer complex legal questions about contract clauses. Traditional methods struggle with long, nuanced texts that often contain irrelevant information. This issue is particularly pressing in the legal industry, where precise interpretation of contracts is crucial. The paper aims to improve LLM performance in generating structured answers from contracts, a task that has significant implications for streamlining legal document analysis. By focusing on this problem, the researchers seek to develop more efficient and accurate AI solutions for contract review, potentially revolutionizing how legal professionals handle document analysis at scale.

2. Methodology

• SUMMARY: The study's methodology centers on a novel two-stage prompt chaining approach. It focuses on four challenging legal questions related to change of control, assignment, insurance, and force majeure clauses. The first stage involves summarizing the relevant aspects of a clause based on the question. The second stage uses this summary to generate structured answers. The researchers compare this approach with single-stage prompts, using GPT-4 and GPT-4-Turbo. They evaluate performance using precision, recall, and exact match accuracy metrics. The use of real-world legal clauses from EDGAR and SEDAR enhances the study's practical relevance. This comprehensive methodology allows for a thorough examination of the proposed approach's effectiveness in real-world scenarios.

3. Key Findings and Contributions

• SUMMARY: The research reveals that two-stage prompts generally outperform single-stage prompts, particularly for complex legal questions. Including answer options in the first stage further enhances performance. However, the approach shows limitations when dealing with high linguistic variation, such as interpreting "utility failures" in force majeure clauses. An interesting observation is that summary formats vary based on question type, with expository styles for reasoning questions and list-like formats for enumeration questions. These findings contribute significantly to the field of legal AI, demonstrating a more effective method for handling complex legal texts. The study's results pave the way for more accurate and efficient AI-assisted contract analysis, potentially transforming legal document review processes.

4. Innovations

• SUMMARY: The paper introduces several innovative approaches to legal question-answering using LLMs. The primary innovation is the application of prompt chaining in the legal domain, breaking down complex tasks into more manageable steps. This method allows for better handling of nuanced legal language. Another key innovation is the tailoring of summaries to specific legal questions and desired structured outputs, which helps in filtering out irrelevant information and focusing on crucial details. The exploration of different prompting strategies to minimize hallucination and improve accuracy is also noteworthy. These innovations collectively represent a significant advancement in applying AI to legal document analysis, offering new possibilities for enhancing the accuracy and efficiency of contract review processes.

5. Challenges and Limitations

• SUMMARY: Despite its promising results, the study faces several challenges and limitations. The approach struggles with high linguistic variation, sometimes requiring exhaustive definitions to improve performance. This is particularly evident in clauses with diverse phrasings, like force majeure. The sensitivity of prompt engineering to specific wording remains a significant challenge, as small changes can lead to large variations in output. The study's focus on only four types of legal questions raises concerns about its generalizability to broader legal domains. Additionally, the reliance on proprietary LLMs like GPT-4 may limit reproducibility. These limitations highlight the complexity of applying AI to legal analysis and underscore areas for future research and improvement.

6. Implications and Applications

• SUMMARY: The research has far-reaching implications for the legal tech industry. It presents a potential breakthrough in improving the efficiency and accuracy of contract analysis at scale. This approach could be particularly valuable in high-stakes environments such as mergers and acquisitions, contract drafting, and risk mitigation. By enabling more accurate AI-assisted analysis of complex legal documents, the method could significantly reduce the time and resources required for contract review. It also opens up possibilities for more sophisticated legal AI tools that can handle nuanced interpretations of contract clauses. The implications extend beyond just efficiency gains, potentially transforming how legal professionals interact with and analyze contractual documents in various legal practices.

7. Future Work

• SUMMARY: The paper outlines several promising directions for future research. These include exploring alternative methods of re-framing legal text beyond summarization, which could potentially address some of the current limitations. Investigating pre-training or fine-tuning LLMs specifically on legal contracts and legal questions is another avenue that could enhance performance. The development of more sophisticated prompt templates incorporating clause definitions is also suggested. Future work will likely examine the robustness of the approach across different versions of generative models. Additionally, refining prompts to address hallucination in legal systems remains a critical area for improvement. These directions aim to further enhance the accuracy and reliability of AI in legal document analysis.

8. Methodology Strengths

• SUMMARY: The study's methodology demonstrates several strengths that enhance its credibility and relevance. The comprehensive comparison of different prompting strategies provides a thorough evaluation of the proposed approach. The careful consideration of legal nuances in prompt design shows a deep understanding of the complexities involved in legal language processing. The use of multiple evaluation metrics (precision, recall, and exact match accuracy) offers a multi-faceted assessment of the model performance. Additionally, the use of real-world legal clauses from reputable sources like EDGAR and SEDAR enhances the practical applicability of the findings. These methodological strengths contribute to the robustness of the study and its potential impact on real-world legal tech applications.

9. Writing and Presentation

• SUMMARY: The paper's writing and presentation are noteworthy for their clarity and structure. The authors provide clear explanations of the methodology, results, and implications, making the complex topic accessible to both technical and legal audiences. The inclusion of specific prompt examples and detailed analysis of particular cases significantly enhances the reader's understanding of the approach and its outcomes. The well-organized structure of the paper allows for easy navigation through different aspects of the research. This clear presentation style not only aids in comprehending the technical aspects of the study but also helps in grasping its broader implications for the field of legal technology and AI-assisted document analysis.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2024 AnsweringQuestionsinStagesPromp	Radha Chitta Adam Roegiest			Answering Questions in Stages: Prompt Chaining for Contract QA				10.48550/arXiv.2410.12840		2024