Can GPT-3 Perform Statutory Reasoning?
Not on the map. Why?
Abstract (arXiv)
Statutory reasoning is the task of reasoning with facts and statutes, which are rules written in natural language by a legislature. It is a basic legal skill. In this paper we explore the capabilities of the most capable GPT-3 model, text-davinci-003, on an established statutory-reasoning dataset called SARA. We consider a variety of approaches, including dynamic few-shot prompting, chain-of-thought prompting, and zero-shot prompting. While we achieve results with GPT-3 that are better than the previous best published results, we also identify several types of clear errors it makes. We investigate why these errors happen. We discover that GPT-3 has imperfect prior knowledge 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 seen during training. We find GPT-3 performs poorly at answering straightforward questions about these simple synthetic statutes.
Key facts. Cited by 73 papers in the map (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 13 Feb 2023.
Builds on
17 of 29 references.
References · 17
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- 2020A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answeringcited by 62
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- 2021Factoring Statutory Reasoning as Language Understanding Challengescited by 20
- 2021Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Modelscited by 9
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