4 papers
Parallel Thinking, Sequential Answering: Bridging NAR and AR for Efficient Reasoning
Qihang Ai, Haiyun Jiang
We study reasoning tasks through a framework that integrates auto-regressive (AR) and non-autoregressive (NAR) language models. AR models, which generate text sequentially, excel a…
Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models
Qihang Ai, Ruizhou Li, Menghui Wang +1
Recent advances in Vision-Language Models (VLMs) have shown promising capabilities in interpreting visualized graph data, offering a new perspective for graph-structured reasoning…
When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning
Qihang Ai, Jianwu Zhou, Haiyun Jiang +2
Graph data is ubiquitous in the physical world, and it has always been a challenge to efficiently model graph structures using a unified paradigm for the understanding and reasonin…
Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks
Yifan Wang, Qingyan Guo, Xinzhe Ni +4
In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform…