2 citations · 4 across the 10 of their papers we have counts for
10 papers
FaStfact: Faster, Stronger Long-Form Factuality Evaluations in LLMs
Yingjia Wan, Haochen Tan, Xiao Zhu +9
Evaluating the factuality of long-form generations from Large Language Models (LLMs) remains challenging due to efficiency bottlenecks and reliability concerns. Prior efforts attem…
Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning
Haiming Wang, Mert Unsal, Xiaohan Lin +37
We introduce Kimina-Prover Preview, a large language model that pioneers a novel reasoning-driven exploration paradigm for formal theorem proving, as showcased in this preview rele…
FormalAlign: Automated Alignment Evaluation for Autoformalization
Jianqiao Lu, Yingjia Wan, Yinya Huang +3
Autoformalization aims to convert informal mathematical proofs into machine-verifiable formats, bridging the gap between natural and formal languages. However, ensuring semantic al…
Online Matching Meets Sampling Without Replacement
Zhiyi Huang, Chui Shan Lee, Jianqiao Lu +1
Sampling without replacement is a natural online rounding strategy for converting fractional bipartite matching into an integral one. In Online Bipartite Matching, we can use the B…
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…
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs
Zhongshen Zeng, Yinhong Liu, Yingjia Wan +16
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes. Howeve…
Process-Driven Autoformalization in Lean 4
Jianqiao Lu, Yingjia Wan, Zhengying Liu +10
Autoformalization, the conversion of natural language mathematics into formal languages, offers significant potential for advancing mathematical reasoning. However, existing effort…
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…
AutoPSV: Automated Process-Supervised Verifier
Jianqiao Lu, Zhiyang Dou, Hongru Wang +4
In this work, we propose a novel method named \textbf{Auto}mated \textbf{P}rocess-\textbf{S}upervised \textbf{V}erifier (\textbf{\textsc{AutoPSV}}) to enhance the reasoning capabil…
MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation
Jianbo Dai, Jianqiao Lu, Yunlong Feng +6
Recent advancements in large language models (LLMs) have greatly improved code generation, specifically at the function level. For instance, GPT-4o has achieved a 91.0\% pass rate…