9 citations · 16 across the 16 of their papers we have counts for
6 papers · 1 filter
OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling
Zhicheng Yang, Yiwei Wang, Yinya Huang +7
Large language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning. Solving realistic optimization (OPT) problems in application scenarios requir…
FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving
Xiaohan Lin, Qingxing Cao, Yinya Huang +5
Formal verification (FV) has witnessed growing significance with current emerging program synthesis by the evolving large language models (LLMs). However, current formal verificati…
Proving Theorems Recursively
Haiming Wang, Huajian Xin, Zhengying Liu +8
Recent advances in automated theorem proving leverages language models to explore expanded search spaces by step-by-step proof generation. However, such approaches are usually base…
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Huajian Xin, Daya Guo, Zhihong Shao +6
Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability. Although large language models (LLMs) show promise in mathem…
ATG: Benchmarking Automated Theorem Generation for Generative Language Models
Xiaohan Lin, Qingxing Cao, Yinya Huang +4
Humans can develop new theorems to explore broader and more complex mathematical results. While current generative language models (LMs) have achieved significant improvement in au…
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
Yinya Huang, Xiaohan Lin, Zhengying Liu +6
Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving. As these two tasks require strict…