A no-fine-tuning ChatGPT pipeline with breadth-first and depth-first tactic searches achieves a 31.15% pass rate on miniF2F in Lean, surpassing most but not all published baselines.
Elaboration in Dependent Type Theory
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abstract
To be usable in practice, interactive theorem provers need to provide convenient and efficient means of writing expressions, definitions, and proofs. This involves inferring information that is often left implicit in an ordinary mathematical text, and resolving ambiguities in mathematical expressions. We refer to the process of passing from a quasi-formal and partially-specified expression to a completely precise formal one as elaboration. We describe an elaboration algorithm for dependent type theory that has been implemented in the Lean theorem prover. Lean's elaborator supports higher-order unification, type class inference, ad hoc overloading, insertion of coercions, the use of tactics, and the computational reduction of terms. The interactions between these components are subtle and complex, and the elaboration algorithm has been carefully designed to balance efficiency and usability. We describe the central design goals, and the means by which they are achieved.
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Simplifying Formal Proof-Generating Models with ChatGPT and Basic Searching Techniques
A no-fine-tuning ChatGPT pipeline with breadth-first and depth-first tactic searches achieves a 31.15% pass rate on miniF2F in Lean, surpassing most but not all published baselines.