3 papers
cs.CV2025
MMLongCite: A Benchmark for Evaluating Fidelity of Long-Context Vision-Language Models
Keyan Zhou, Zecheng Tang, Lingfeng Ming +8
The rapid advancement of large vision language models (LVLMs) has led to a significant expansion of their context windows. However, an extended context window does not guarantee th…
cs.CL2025
Revealing and Mitigating Over-Attention in Knowledge Editing
Pinzheng Wang, Zecheng Tang, Keyan Zhou +3
Large Language Models have demonstrated superior performance across a wide range of tasks, but they still exhibit undesirable errors due to incorrect knowledge learned from the tra…
cs.CL2024
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
Zecheng Tang, Keyan Zhou, Juntao Li +3
Long-context models (LCMs) have made remarkable strides in recent years, offering users great convenience for handling tasks that involve long context, such as document summarizati…