8 papers
SWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering?
Yuxuan Sun, Yuze Zhao, Yufeng Wang +6
Evaluating software engineering capabilities has become a core component of modern large language models (LLMs); however, the key bottleneck hindering further scaling lies not in t…
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…
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…
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…
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…