465 citations · 688 across the 32 of their papers we have counts for
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cs.AI2025★ 2 cited
From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs
Jialun Cao, Yaojie Lu, Meiziniu Li +9
The research in AI-based formal mathematical reasoning has shown an unstoppable growth trend. These studies have excelled in mathematical competitions like IMO and have made signif…
cs.AI2024★ 1 cited
DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation
Qiming Zhu, Jialun Cao, Yaojie Lu +4
Code benchmarks such as HumanEval are widely adopted to evaluate the capabilities of Large Language Models (LLMs), providing insights into their strengths and weaknesses. However,…
cs.AI2024
CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution
Ruiyang Xu, Jialun Cao, Yaojie Lu +6
Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models' (LLMs) coding capabilities. However, there is an unignorable programming language bias in ex…