Formalize Once, Edit the Rest: Efficient Lean-Based Answer Selection for Math Reasoning
📰 ArXiv cs.AI
Learn to efficiently select answers for math reasoning using Lean-based formalization and large language models, improving test-time scaling
Action Steps
- Formalize LLM outputs using Lean to enable machine-checkable rigor
- Edit and refine formalized outputs to reduce errors
- Apply Lean-based answer selection to K sampled candidate answers
- Test and evaluate the efficiency of the approach using metrics such as accuracy and scalability
- Integrate the Lean-based approach with LLMs to improve overall performance
Who Needs to Know This
Researchers and developers working on math reasoning and large language models can benefit from this approach to improve answer selection efficiency and accuracy
Key Insight
💡 Formalizing LLM outputs once and editing the rest can significantly improve answer selection efficiency for math reasoning
Share This
🤖💡 Improve math reasoning answer selection with Lean-based formalization and LLMs! 🚀
Key Takeaways
Learn to efficiently select answers for math reasoning using Lean-based formalization and large language models, improving test-time scaling
Full Article
Title: Formalize Once, Edit the Rest: Efficient Lean-Based Answer Selection for Math Reasoning
Abstract:
arXiv:2606.15972v1 Announce Type: cross Abstract: With large language models (LLMs) increasingly applied to mathematical reasoning, formal proof assistants such as Lean can be leveraged to verify reasoning outputs with machine-checkable rigor, enabling use cases such as answer selection in test-time scaling with K sampled candidate answers. However, employing Lean requires that LLM outputs, originally in natural language, first be formalized. Existing Lean-based answer-selection work uses an aut
Abstract:
arXiv:2606.15972v1 Announce Type: cross Abstract: With large language models (LLMs) increasingly applied to mathematical reasoning, formal proof assistants such as Lean can be leveraged to verify reasoning outputs with machine-checkable rigor, enabling use cases such as answer selection in test-time scaling with K sampled candidate answers. However, employing Lean requires that LLM outputs, originally in natural language, first be formalized. Existing Lean-based answer-selection work uses an aut
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