@inproceedings{roegiest-etal-2023-questions,
title = "Questions about Contracts: Prompt Templates for Structured Answer Generation",
author = "Roegiest, Adam and
Chitta, Radha and
Donnelly, Jonathan and
Lash, Maya and
Vtyurina, Alexandra and
Longtin, Francois",
editor = "Preo{\textcommabelow{t}}iuc-Pietro, Daniel and
Goanta, Catalina and
Chalkidis, Ilias and
Barrett, Leslie and
Spanakis, Gerasimos (Jerry) and
Aletras, Nikolaos",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nllp-1.8",
doi = "10.18653/v1/2023.nllp-1.8",
pages = "62--72",
abstract = "Finding the answers to legal questions about specific clauses in contracts is an important 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. In this paper, we present an examination of using large language models to produce (partially) structured answers to legal questions; primarily in the form of multiple choice and multiple select. We first show that traditional semantic matching is unable to perform this task at acceptable accuracy and then show how question specific prompts can achieve reasonable accuracy across a range of generative models. Finally, we show that much of this effectiveness can be maintained when generalized prompt templates are used rather than question specific ones.",
}
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<abstract>Finding the answers to legal questions about specific clauses in contracts is an important 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. In this paper, we present an examination of using large language models to produce (partially) structured answers to legal questions; primarily in the form of multiple choice and multiple select. We first show that traditional semantic matching is unable to perform this task at acceptable accuracy and then show how question specific prompts can achieve reasonable accuracy across a range of generative models. Finally, we show that much of this effectiveness can be maintained when generalized prompt templates are used rather than question specific ones.</abstract>
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%0 Conference Proceedings
%T Questions about Contracts: Prompt Templates for Structured Answer Generation
%A Roegiest, Adam
%A Chitta, Radha
%A Donnelly, Jonathan
%A Lash, Maya
%A Vtyurina, Alexandra
%A Longtin, Francois
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Goanta, Catalina
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Spanakis, Gerasimos (Jerry)
%Y Aletras, Nikolaos
%S Proceedings of the Natural Legal Language Processing Workshop 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F roegiest-etal-2023-questions
%X Finding the answers to legal questions about specific clauses in contracts is an important 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. In this paper, we present an examination of using large language models to produce (partially) structured answers to legal questions; primarily in the form of multiple choice and multiple select. We first show that traditional semantic matching is unable to perform this task at acceptable accuracy and then show how question specific prompts can achieve reasonable accuracy across a range of generative models. Finally, we show that much of this effectiveness can be maintained when generalized prompt templates are used rather than question specific ones.
%R 10.18653/v1/2023.nllp-1.8
%U https://aclanthology.org/2023.nllp-1.8
%U https://doi.org/10.18653/v1/2023.nllp-1.8
%P 62-72
Markdown (Informal)
[Questions about Contracts: Prompt Templates for Structured Answer Generation](https://aclanthology.org/2023.nllp-1.8) (Roegiest et al., NLLP-WS 2023)
ACL