941 citations · 5.4k across the 87 of their papers we have counts for
90 papers
Can Machines Really See Objects in Images? A Study Based on Syntactic Distance and Visual Self-Referential Instances
Xingyu Peng, Junran Wu, Yue Hou +9
Can a vision model truly see an object, or does it only fit surface-level visual cues? Following Wittgenstein's view that the limits of language are the limits of the world, we vie…
Self-Boosting Large Language Models with Synthetic Preference Data
Qingxiu Dong, Li Dong, Xingxing Zhang +2
Through alignment with human preferences, Large Language Models (LLMs) have advanced significantly in generating honest, harmless, and helpful responses. However, collecting high-q…
Differential Transformer
Tianzhu Ye, Li Dong, Yuqing Xia +4
Transformer tends to overallocate attention to irrelevant context. In this work, we introduce Diff Transformer, which amplifies attention to the relevant context while canceling no…
Direct Preference Knowledge Distillation for Large Language Models
Yixing Li, Yuxian Gu, Li Dong +3
In the field of large language models (LLMs), Knowledge Distillation (KD) is a critical technique for transferring capabilities from teacher models to student models. However, exis…
You Only Cache Once: Decoder-Decoder Architectures for Language Models
Yutao Sun, Li Dong, Yi Zhu +6
We introduce a decoder-decoder architecture, YOCO, for large language models, which only caches key-value pairs once. It consists of two components, i.e., a cross-decoder stacked u…
Multi-Head Mixture-of-Experts
Xun Wu, Shaohan Huang, Wenhui Wang +1
Sparse Mixtures of Experts (SMoE) scales model capacity without significant increases in training and inference costs, but exhibits the following two issues: (1) Low expert activat…