1 citations · 1 across the 3 of their papers we have counts for
7 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…
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…
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…
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data
Yejie Wang, Keqing He, Dayuan Fu +11
Recently, there has been a growing interest in studying how to construct better code instruction tuning data. However, we observe Code models trained with these datasets exhibit hi…
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…