claim: Large language models can be used to estimate contractual meaning in context, quantify ambiguity, and help adjudicators reason about extrinsic evidence at far lower cost than traditional approaches.

paper_title: Generative Interpretation

evidence_quote: [p. 2] ARBEL & HOFFMAN Generative Interpretation Yonathan A. Arbel & David A. Hoffman* 99 N.Y.U. L. REV. __ (forthcoming 2024) [DRAFT October 27, 2023] 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...

citation: Yonathan A. Arbel & David Hoffman, Generative Interpretation, NYU Law Review (2024).

confidence: machine-linked

limitations: Machine-linked claim. Use the evidence quote and PDF before treating it as a quotation or as a complete statement of the paper's position.


claim: Large Language Models (LLMs) introduce "Generative Interpretation," a paradigm shift in legal text analysis. This approach enables AI to parse contracts, identify ambiguities, and predict judicial outcomes, offering a potentially cheaper, more accurate, and accessible method than traditional textualism or contextualism. He posits that generative interpretation can resolve long-standing interpretive debates, enhance access to justice, and fundamentally re-equip legal theory for AI's role as an active interpretive agent in contract law.

evidence_span: [p. 2] ARBEL & HOFFMAN Generative Interpretation Yonathan A. Arbel & David A. Hoffman* 99 N.Y.U. L. REV. __ (forthcoming 2024) [DRAFT October 27, 2023] 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...


claim: Large Language Models (LLMs) can now interpret legal texts, a capability he terms "Generative Interpretation." This signifies a paradigm shift where AI becomes an active interpretive agent, a development for which current legal theory is unprepared. He introduces generative interpretation as a new approach using LLMs to estimate contractual meaning, ascertain ordinary meaning, quantify ambiguity, and fill gaps. This method aims to offer courts a cheaper, more accurate way to discern parties' intentions, potentially resolving the textualist-contextualist stalemate and providing a more accessible and transparent tool for contract analysis.