3 papers
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…
cs.CL2024
MemLong: Memory-Augmented Retrieval for Long Text Modeling
Weijie Liu, Zecheng Tang, Juntao Li +2
Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs…
cs.CL2022
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change
Zhaochen Su, Zecheng Tang, Xinyan Guan +3
Recent research has revealed that neural language models at scale suffer from poor temporal generalization capability, i.e., the language model pre-trained on static data from past…