20 citations · 20 across the 2 of their papers we have counts for
7 papers · 1 filter
MiniLLM: On-Policy Distillation of Large Language Models
Yuxian Gu, Li Dong, Furu Wei +1
Knowledge Distillation (KD) is a promising technique for reducing the high computational demand of large language models (LLMs). However, previous KD methods are primarily applied…
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…
Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
Wenshan Wu, Shaoguang Mao, Yadong Zhang +4
Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a crucial a…
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…
KOSMOS-2.5: A Multimodal Literate Model
Tengchao Lv, Yupan Huang, Jingye Chen +13
The automatic reading of text-intensive images represents a significant advancement toward achieving Artificial General Intelligence (AGI). In this paper we present KOSMOS-2.5, a m…