activity
20212026
most citedEDITS: Modeling and Mitigating Data Bias for Graph Neural Networks

128 citations · 350 across the 37 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL2026

Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshaping

Zhenyu Lei, Qiong Wu, Jianxiong Dong +4

Large language models (LLMs) often exhibit flawed reasoning ability that undermines reliability. Existing approaches to improving reasoning typically treat it as a general and mono…

cs.CL2025

Learning from Diverse Reasoning Paths with Routing and Collaboration

Zhenyu Lei, Zhen Tan, Song Wang +4

Advances in large language models (LLMs) significantly enhance reasoning capabilities but their deployment is restricted in resource-constrained scenarios. Knowledge distillation a…

cs.CL2025

Harnessing Large Language Models for Disaster Management: A Survey

Zhenyu Lei, Yushun Dong, Weiyu Li +3

Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields. Among their practical applications, LLMs ha…

cs.CL2024

CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models

Song Wang, Peng Wang, Tong Zhou +3

As Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks, concerns regarding the potential negative societal impacts of L…

cs.CL2024

Knowledge Graph-Enhanced Large Language Models via Path Selection

Haochen Liu, Song Wang, Yaochen Zhu +2

Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. However, they are known to generate factually inaccurate outputs, a.k.a. the h…