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Statutory reasoning is the task of reasoning with facts and statutes, whichare rules written in natural language by a legislature. It is a basic legalskill. In this paper we explore the capabilities of the most capable GPT-3model, text-davinci-003, on an established statutory-reasoning dataset calledSARA. We consider a variety of approaches, including dynamic few-shotprompting, chain-of-thought prompting, and zero-shot prompting. While weachieve results with GPT-3 that are better than the previous best publishedresults, we also identify several types of clear errors it makes. Weinvestigate why these errors happen. We discover that GPT-3 has imperfect priorknowledge of the actual U.S. statutes on which SARA is based. More importantly,we create simple synthetic statutes, which GPT-3 is guaranteed not to have seenduring training. We find GPT-3 performs poorly at answering straightforwardquestions about these simple synthetic statutes.

Can GPT-3 Perform Statutory Reasoning?