Generative Interpretation
Yonathan A. Arbel, David Hoffman

TL;DR
This paper proposes generative interpretation, leveraging large language models to improve contractual meaning estimation by aiding factfinding, quantifying ambiguity, and assessing evidence, potentially transforming judicial interpretation practices.
Contribution
It introduces a novel approach using AI models for contract interpretation, demonstrating practical applications and discussing implications for legal practice and theory.
Findings
AI models help ascertain contract meaning in context
Models quantify ambiguity and evaluate extrinsic evidence
Generative interpretation can improve judicial decision-making
Abstract
We introduce generative interpretation, a new approach to estimating contractual meaning using large language models. As AI triumphalism is the order of the day, we proceed by way of grounded case studies, each illustrating the capabilities of these novel tools in distinct ways. Taking well-known contracts opinions, and sourcing the actual agreements that they adjudicated, we show that AI models can help factfinders ascertain ordinary meaning in context, quantify ambiguity, and fill gaps in parties' agreements. We also illustrate how models can calculate the probative value of individual pieces of extrinsic evidence. After offering best practices for the use of these models given their limitations, we consider their implications for judicial practice and contract theory. Using LLMs permits courts to estimate what the parties intended cheaply and accurately, and as such generative…
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Taxonomy
TopicsArtificial Intelligence in Law · Comparative and International Law Studies · Law, Economics, and Judicial Systems
