Identifying open-texture in regulations using LLMs

Abstract

Open-texture—e.g. vague, ambiguous, under-specified, or abstract terms—in regulatory documents lead to inconsistent interpretation, and are an obstacle to the automatic processing of regulation by computers. Identifying which parts of a legal text fall under open-texture is therefore a necessary requirement to make progress in automating the law. In this paper, we propose that large language models (LLMs) might provide an effective way to automatically detect open-texture in legal texts. We first investigate the obstacles by situating Show more