A Search for Prompts: Generating Structured Answers from Contracts
Adam Roegiest, Radha Chitta, Jonathan Donnelly, Maya Lash and, Alexandra Vtyurina, Fran\c{c}ois Longtin

TL;DR
This paper explores prompt engineering for legal question answering using GPT-3.5-Turbo, demonstrating that well-designed prompts outperform semantic matching in accuracy for extracting structured answers from contracts.
Contribution
It introduces a prompt-based approach for legal QA that improves accuracy over semantic matching and discusses methods to enhance reliability and performance.
Findings
Prompt templates significantly outperform semantic matching in accuracy.
In-context learning improves the performance and reliability of prompt-based legal QA.
Prompt tweaks and in-context learning enhance structured answer extraction.
Abstract
In many legal processes being able to action on the concrete implication of a legal question can be valuable to automating human review or signalling certain conditions (e.g., alerts around automatic renewal). To support such tasks, we present a form of legal question answering that seeks to return one (or more) fixed answers for a question about a contract clause. After showing that unstructured generative question answering can have questionable outcomes for such a task, we discuss our exploration methodology for legal question answering prompts using OpenAI's \textit{GPT-3.5-Turbo} and provide a summary of insights. Using insights gleaned from our qualitative experiences, we compare our proposed template prompts against a common semantic matching approach and find that our prompt templates are far more accurate despite being less reliable in the exact response return. With some…
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Taxonomy
TopicsTopic Modeling · Artificial Intelligence in Law · Natural Language Processing Techniques
