Proposal for Collaborative Optimization — Complementary Enhancement Suggestions for xVerify #46
Description
Hello! First, I would like to express my sincere admiration for the math-verify project. After thoroughly reviewing the documentation and test cases, I found that the regex-based verification method in your project demonstrates exceptional efficiency and precision in evaluating mathematical problem-solving scenarios. The design philosophy of this lightweight solution is truly inspiring.
Our team has also been exploring LLM evaluation frameworks and recently developed xVerify (GitHub | Hugging Face), a framework designed for evaluating multiple types of objective questions (math/MCQ/classification/short-answer). Unlike math-verify’s focus, we employ an LLM-as-a-judge approach to handle complex scenarios requiring semantic understanding. Notably, we observed inherent complementarity between regex and LLM methods for math problem evaluation: regex excels in structured problems with efficiency and stability, while LLMs address open-ended responses.
Would you consider the following collaboration avenues?
- Integrate xVerify as a plugin module into the math-verify ecosystem
- Co-design hybrid validation strategies (e.g., regex-first + LLM fallback)
- Jointly develop a comprehensive benchmark for cross-method evaluation
If the maintainers find value in these ideas, we would gladly contribute code via PRs to refine use cases for both approaches. Regardless of your decision, we will continue to support and recommend this outstanding project!
Thank you for your contributions to the open-source community, and we look forward to your feedback!