Summary of Fvel: Interactive Formal Verification Environment with Large Language Models Via Theorem Proving, by Xiaohan Lin et al.
FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving
by Xiaohan Lin, Qingxing Cao, Yinya Huang, Haiming Wang, Jianqiao Lu, Zhengying Liu, Linqi Song, Xiaodan Liang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Formal verification (FV) has gained importance with the emergence of large language models (LLMs). However, current FV relies on symbolic verifiers or hand-crafted rules, limiting its scope. This paper proposes FVEL, an interactive Formal Verification Environment that leverages LLMs for neural automated theorem proving. FVEL transforms code to be verified into Isabelle and conducts verification via neural theorem proving with LLMs. The joined paradigm combines the rigorous rules of Isabelle with the flexibility of LLMs. To achieve this goal, the authors extract a large-scale dataset, FVELER3, containing 758 theories, 29,125 lemmas, and 200,646 proof steps. Benchmarking FVEL in the FVEL environment shows that fine-tuning LLMs with FVELER improves performance on Code2Inv and SV-COMP benchmarks. The results demonstrate a 12-17% increase in problem-solving capabilities for Mistral-7B and Llama3-8B, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to write a program that is perfect and bug-free from the start. This is called formal verification. Right now, there are limitations to doing this automatically. This paper proposes a new way to make it more efficient by using large language models (LLMs) to help with the process. The authors created a system called FVEL that can take code and transform it into another format to be verified. They also created a big dataset called FVELER3 to test their system. The results show that their method is effective in solving problems, making it more efficient than current methods. |
Keywords
* Artificial intelligence * Fine tuning
