Learning to Disprove: Formal Counterexample Generation with Large Language Models

1. 1Fine-tune large language models on a dataset of mathematical statements and counterexamples
2. 2Use the fine-tuned model to generate counterexamples for new, unseen statements
3. 3Evaluate the generated counterexamples for correctness and validity
4. 4Refine the model through iterative fine-tuning and evaluation

Researchers and AI engineers working on mathematical reasoning and formal verification can benefit from this approach to improve the robustness of their models and identify false statements

💡 Large language models can be fine-tuned to generate formal counterexamples, complementing proof construction and improving mathematical reasoning

🤖 Fine-tuning LLMs to generate formal counterexamples for mathematical statements 📝