1 citations · 1 across the 4 of their papers we have counts for
5 papers
TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data
Changjiang Jiang, Fengchang Yu, Haihua Chen +2
Complex reasoning over tabular data is crucial in real-world data analysis, yet large language models (LLMs) often underperform due to complex queries, noisy data, and limited nume…
Chem-R: Learning to Reason as a Chemist
Weida Wang, Benteng Chen, Di Zhang +14
Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories…
GSPR: Aligning LLM Safeguards as Generalizable Safety Policy Reasoners
Haoran Li, Jingru Zeng, Yulin Chen +8
As large language models (LLMs) are integrated into numerous applications, LLMs' safety becomes critical for both application developers and intended users. Currently, great effort…
Unlocking Multimodal Mathematical Reasoning via Process Reward Model
Ruilin Luo, Zhuofan Zheng, Yifan Wang +9
Process Reward Models (PRMs) have shown promise in enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) through Test-Time Scaling (TTS). However, their…
Math-PUMA: Progressive Upward Multimodal Alignment to Enhance Mathematical Reasoning
Wenwen Zhuang, Xin Huang, Xiantao Zhang +1
Multimodal Large Language Models (MLLMs) excel in solving text-based mathematical problems, but they struggle with mathematical diagrams since they are primarily trained on natural…