Generative Interpretation
Yonathan Arbel, David A. Hoffman
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 large
language models permits courts to estimate what the parties intended cheaply and
accurately, and as such generative interpretation unsettles the current interpretative
stalemate. Their use responds to efficiency-minded textualists and justice-oriented
contextualists, who argue about whether parties will prefer cost and certainty or
accuracy and fairness. Parties—and courts—would prefer a middle path, in which
adjudicators strive to predict what the contract really meant, admitting just enough
context to approximate reality while avoiding unguided and biased assimilation of
evidence. As generative interpretation offers this possibility, we argue it can become
the new workhorse of contractual interpretation.
