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Florida Law Review

Abstract

The textualist turn is increasingly an empirical one—an inquiry into ordinary meaning in the sense of what is commonly or typically ascribed to a given word or phrase. Such an inquiry is inherently empirical. And empirical questions call for replicable evidence produced by transparent methods—not bare human intuition or an arbitrary preference for one dictionary definition over another. Both scholars and judges have begun to make this turn. They have started to adopt the tools used in the field of corpus linguistics—a field that studies language usage by examining large databases (corpora) of naturally occurring language.

This turn is now being challenged by a proposal to use a simpler, now-familiar large language model (LLM)—AI-driven LLMs like ChatGPT. The proposal began with two recent law review articles. And it caught fire—and a load of media attention—with concurring opinions by Eleventh Circuit Judge Kevin Newsom in Snell v. United Specialty Insurance Co. and United States v. Deleon. These concurring opinions proposed to use ChatGPT and other LLM AIs to generate evidence of relevance to empirical questions of ordinary meaning—on whether the installation of in-ground trampolines falls under the ordinary meaning of “landscaping” as used in an insurance policy and whether a robber “physically restrains” a victim under the U.S. Sentencing Guidelines when he holds him at gunpoint. Judge Newsom developed a case for relying on such evidence—and for rejecting the methodology of corpus linguistics—based, in part, on recent legal scholarship. And he presented a series of AI queries and responses that he proffered as “datapoints” to be considered “alongside” dictionaries and other evidence of ordinary meaning.

The proposal is alluring. And in some ways, it seems inevitable that AI tools will be part of the future of an empirical analysis of ordinary meaning. But existing AI tools are not up to the task. Their results present a form of artificial intuition—not empirical analysis. And they are in no position to produce reliable datapoints on questions like the ones in Snell or Deleon. We show how AI falls short and corpus tools deliver on core components of empirical inquiry. We present a transparent, replicable means of developing relevant data on the issues in Snell and Deleon. We respond to the counterposition developed in these opinions and the articles they rely on. And we conclude by exploring the elements of a future in which the strengths of AI-driven LLMs could be deployed in a corpus analysis.

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