LLMS FOR FORMAL THEOREM PROVING – HUMAN–MACHINE COLLABORATION ON PROOFS

This research explores the value of large language models (LLMs) for improving human-machine collaboration during formal theorem proving.A central issue is identifying interaction strategies that use LLM capabilities and maintain mathematical rigor.We need qualitative data from user studies and quantitative metrics on proof completion rates and accuracy.This paper explores how large language models (LLMs) facilitate human-machine collaboration in formal theorem proving.By analyzing interactions between human users and LLMs within proof assistants like Lean and Coq, this research demonstrates how AI can augment human reasoning without sacrificing mathematical rigor.Establishing reliable protocols for collaborative theorem proving has significant implications for both theoretical mathematics and the formal verification of complex software systems.