189 citations · 196 across the 22 of their papers we have counts for
7 papers · 1 filter
LOGO -- Long cOntext aliGnment via efficient preference Optimization
Zecheng Tang, Zechen Sun, Juntao Li +2
Long-context models(LCMs) have shown great potential in processing long input sequences(even more than 100M tokens) conveniently and effectively. With significant progress, recent…
Revealing and Mitigating the Local Pattern Shortcuts of Mamba
Wangjie You, Zecheng Tang, Juntao Li +2
Large language models (LLMs) have advanced significantly due to the attention mechanism, but their quadratic complexity and linear memory demands limit their performance on long-co…
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…
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…
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
Dan Qiao, Yi Su, Pinzheng Wang +18
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment re…
Rethinking Negative Instances for Generative Named Entity Recognition
Yuyang Ding, Juntao Li, Pinzheng Wang +3
Large Language Models (LLMs) have demonstrated impressive capabilities for generalizing in unseen tasks. In the Named Entity Recognition (NER) task, recent advancements have seen t…
StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
Zecheng Tang, Chenfei Wu, Zekai Zhang +8
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model…