1 citations · 1 across the 9 of their papers we have counts for
9 papers
Do Large Language Models Excel in Complex Logical Reasoning with Formal Language?
Jin Jiang, Jianing Wang, Yuchen Yan +5
Large Language Models (LLMs) have been shown to achieve breakthrough performance on complex logical reasoning tasks. Nevertheless, most existing research focuses on employing forma…
VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models
Yuchen Yan, Jin Jiang, Zhenbang Ren +9
Large reasoning models such as OpenAI o1 and DeepSeek-R1 have demonstrated remarkable performance in complex reasoning tasks. A critical component of their training is the incorpor…
Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning
Jianing Wang, Jin Jiang, Yang Liu +2
In this paper, we introduce a new \emph{process prejudge} strategy in LLM reasoning to demonstrate that bootstrapping with process prejudge allows the LLM to adaptively anticipate…
InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models
Yuchen Yan, Yongliang Shen, Yang Liu +4
Advanced reasoning in large language models has achieved remarkable performance on challenging tasks, but the prevailing long-context reasoning paradigm faces critical limitations:…
The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights
Yufang Liu, Yao Du, Tao Ji +6
Recent research has increasingly focused on multimodal mathematical reasoning, particularly emphasizing the creation of relevant datasets and benchmarks. Despite this, the role of…
MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps through Fill-in-the-Middle Task
Yuchen Yan, Yongliang Shen, Yang Liu +5
Mathematical reasoning represents a critical frontier in advancing large language models (LLMs). While step-by-step approaches have emerged as the dominant paradigm for mathematica…
LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning
Jin Jiang, Yuchen Yan, Yang Liu +6
In this paper, we propose a new data synthesis method called \textbf{LogicPro}, which leverages LeetCode-style algorithm \underline{Pro}blems and their corresponding \underline{Pro…
S^3cMath: Spontaneous Step-level Self-correction Makes Large Language Models Better Mathematical Reasoners
Yuchen Yan, Jin Jiang, Yang Liu +5
Self-correction is a novel method that can stimulate the potential reasoning abilities of large language models (LLMs). It involves detecting and correcting errors during the infer…
EAGLE: Elevating Geometric Reasoning through LLM-empowered Visual Instruction Tuning
Zhihao Li, Yao Du, Yang Liu +6
Multi-modal Large Language Models (MLLMs) have advanced greatly in general tasks. However, they still face challenges in geometric reasoning, a task that requires synergistic integ…