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Experimental results from applying GPT-4 to an unpublished formal language

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arxiv 2305.12196 v1 pith:OICZUZXZ submitted 2023-05-20 cs.CL cs.LOmath.LO

classification cs.CLcs.LOmath.LO
keywords languagesystemtasksanswercompleteformalgpt-4unpublished
verification ladder T0 review T1 audit T2 compute T3 formal
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Can large language models be used to complete mathematical tasks that are traditionally performed either manually or with the aid of theorem provers? To answer this question, a state-of-the-art system, GPT-4, was provided with a concise natural language specification for a previously unpublished formal system and asked to complete a number of tasks, from stating function and type definitions to proving simple theorems and verifying user-supplied proofs. The system completed all tasks successfully, showed extensive domain knowledge, invented helpful new syntax and semantics, and exhibited generalization and inference abilities. So the answer seems to be: yes.

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  1. Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

    cs.CL 2025-05 reject novelty 5.0 of 10

    A grammar-based model of LLM-generated SMT-LIB code produces uncertainty signals that predict formalization errors on some reasoning tasks, with fused signals giving large error reductions only in an in-sample evaluation.

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