9 citations · 30 across the 30 of their papers we have counts for
12 papers · 1 filter
FormalRx: Rectify and eXamine Semantic Failures in Autoformalization
Haocheng Wang, Baiyu Huang, Yingjia Wan +4
The veracious semantic alignment in autoformalization is significant for formal mathematical reasoning. However, existing evaluations provide only opaque binary verdicts or scalar…
DeReason: A Difficulty-Aware Curriculum Improves Decoupled SFT-then-RL Training for General Reasoning
Hanxu Hu, Yuxuan Wang, Maggie Huan +4
Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics…
LEXam: Benchmarking Legal Reasoning on 340 Law Exams
Yu Fan, Jingwei Ni, Jakob Merane +14
Long-form legal reasoning remains a key challenge for large language models (LLMs) in spite of recent advances in test-time scaling. To address this, we introduce LEXam, a novel be…
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…
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…
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…
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…
CLOMO: Counterfactual Logical Modification with Large Language Models
Yinya Huang, Ruixin Hong, Hongming Zhang +6
In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs). Our primary objective is to cultivate the counterfactual thought pr…
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Jing Xiong, Jianhao Shen, Ye Yuan +11
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models. However, current ATP benchma…
Discourse-Aware Graph Networks for Textual Logical Reasoning
Yinya Huang, Lemao Liu, Kun Xu +3
Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations…
DAGN: Discourse-Aware Graph Network for Logical Reasoning
Yinya Huang, Meng Fang, Yu Cao +2
Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting amo…
REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement
Yinya Huang, Meng Fang, Xunlin Zhan +3
When answering a question, people often draw upon their rich world knowledge in addition to the particular context. While recent works retrieve supporting facts/evidence from commo…