Pipeline for Verifying LLM-Generated Mathematical Solutions
arXiv:2602.20770v1 Announce Type: new Abstract: With the growing popularity of Large Reasoning Models and their results in solving mathematical problems, it becomes crucial to measure their capabilities. We introduce a pipeline for both automatic and interactive verification as a more accurate alternative to only checking the answer which is currently the most popular approach for benchmarks. The pipeline can also be used as a generator of correct solutions both in formal and informal languages. 3 AI agents, which can be chosen for the benchmark accordingly, are included in the structure. The key idea is the use of prompts to obtain the solution in the specific form which allows for easier verification using proof assistants and possible use of small models ($\le 8B$). Experiments on several datasets suggest low probability of False Positives. The open-source implementation with instructions on setting up a server is available at https://github.com/LogicEnj/lean4_verification_pipeline
arXiv:2602.20770v1 Announce Type: new Abstract: With the growing popularity of Large Reasoning Models and their results in solving mathematical problems, it becomes crucial to measure their capabilities. We introduce a pipeline for both automatic and interactive verification as a more accurate alternative to only checking the answer which is currently the most popular approach for benchmarks. The pipeline can also be used as a generator of correct solutions both in formal and informal languages. 3 AI agents, which can be chosen for the benchmark accordingly, are included in the structure. The key idea is the use of prompts to obtain the solution in the specific form which allows for easier verification using proof assistants and possible use of small models ($\le 8B$). Experiments on several datasets suggest low probability of False Positives. The open-source implementation with instructions on setting up a server is available at https://github.com/LogicEnj/lean4_verification_pipeline.
Executive Summary
This article introduces a pipeline for verifying Large Language Model (LLM)-generated mathematical solutions, providing a more accurate alternative to solely checking the answer. The pipeline utilizes prompts to obtain solutions in a specific format, allowing for easier verification using proof assistants and smaller models. Experiments demonstrate a low probability of false positives, and an open-source implementation is available. The pipeline can also generate correct solutions in formal and informal languages, making it a valuable tool for mathematical problem-solving.
Key Points
- ▸ Introduction of a pipeline for verifying LLM-generated mathematical solutions
- ▸ Use of prompts to obtain solutions in a specific format for easier verification
- ▸ Inclusion of three AI agents for benchmarking and verification
Merits
Improved Verification Accuracy
The pipeline provides a more accurate alternative to solely checking the answer, reducing the likelihood of false positives and increasing confidence in LLM-generated solutions.
Demerits
Limited Model Capacity
The pipeline's reliance on smaller models (≤ 8B) may limit its applicability to more complex mathematical problems that require larger models.
Expert Commentary
The introduction of this pipeline marks a significant step forward in verifying LLM-generated mathematical solutions. By leveraging prompts and smaller models, the pipeline demonstrates a high degree of accuracy and potential for practical applications. However, further research is needed to address the limitations of model capacity and to explore the pipeline's applicability to more complex mathematical problems. The open-source implementation and availability of the pipeline will likely facilitate collaboration and advancements in this area, ultimately contributing to the development of more reliable and trustworthy LLM-generated mathematical solutions.
Recommendations
- ✓ Further research should be conducted to expand the pipeline's capacity to handle larger models and more complex mathematical problems
- ✓ The pipeline should be tested and validated in various applications, such as education and research, to assess its practical implications and potential impact