Generative Interpretation
Yonathan A. Arbel David 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 LLMs 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.