667 citations · 1.2k across the 29 of their papers we have counts for
6 papers · 1 filter
Interleaved-Modal Chain-of-Thought
Jun Gao, Yongqi Li, Ziqiang Cao +1
Chain-of-Thought (CoT) prompting elicits large language models (LLMs) to produce a series of intermediate reasoning steps before arriving at the final answer. However, when transit…
AIM: Let Any Multi-modal Large Language Models Embrace Efficient In-Context Learning
Jun Gao, Qian Qiao, Ziqiang Cao +2
In-context learning (ICL) facilitates Large Language Models (LLMs) exhibiting emergent ability on downstream tasks without updating billions of parameters. However, in the area of…
SelfCP: Compressing Over-Limit Prompt via the Frozen Large Language Model Itself
Jun Gao, Ziqiang Cao, Wenjie Li
Long prompt leads to huge hardware costs when using transformer-based Large Language Models (LLMs). Unfortunately, many tasks, such as summarization, inevitably introduce long docu…
Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization
Shichao Sun, Ruifeng Yuan, Ziqiang Cao +2
Large language models (LLMs) have demonstrated the capacity to improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the init…
UniICL: An Efficient Unified Framework Unifying Compression, Selection, and Generation
Jun Gao, Qi Lv, Zili Wang +3
In-context learning (ICL) enhances the reasoning abilities of Large Language Models (LLMs) by prepending a few demonstrations. It motivates researchers to introduce more examples t…
CoUDA: Coherence Evaluation via Unified Data Augmentation
Dawei Zhu, Wenhao Wu, Yifan Song +3
Coherence evaluation aims to assess the organization and structure of a discourse, which remains challenging even in the era of large language models. Due to the scarcity of annota…