6 citations · 16 across the 18 of their papers we have counts for
8 papers · 1 filter
IntAttention: A Fully Integer Attention Pipeline for Efficient Edge Inference
Wanli Zhong, Haibo Feng, Zirui Zhou +2
Deploying Transformer models on edge devices is limited by latency and energy budgets. While INT8 quantization effectively accelerates the primary matrix multiplications, it expose…
VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation
Yubo Sun, Chunyi Peng, Yukun Yan +5
Visual Retrieval-Augmented Generation (VRAG) has emerged as a promising paradigm for equipping Vision-Language Models (VLMs) with external visual evidence, enabling them to go beyo…
KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG
Yongjian Li, HaoCheng Chu, Yukun Yan +7
Retrieval-Augmented Generation (RAG) equips large language models with external knowledge and is central to knowledge-intensive tasks. As RAG systems enter real-world use, generato…
Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging
Shi Jie Yu, Sehyun Choi
Checkpoint merging is a technique for combining multiple model snapshots into a single superior model, potentially reducing training time for large language models. This paper expl…
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
Yuxuan Chen, Dewen Guo, Sen Mei +12
Retrieval-Augmented Generation (RAG) significantly enhances the performance of large language models (LLMs) in downstream tasks by integrating external knowledge. To facilitate res…
RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts
Mingyan Wu, Zhenghao Liu, Yukun Yan +5
Retrieval-Augmented Generation (RAG) enhances the performance of Large Language Models (LLMs) by incorporating external knowledge. However, LLMs still encounter challenges in effec…
ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
Sijia Yao, Pengcheng Huang, Zhenghao Liu +4
Large language models (LLMs) have demonstrated significant potential in enhancing dense retrieval through query augmentation. However, most existing methods treat the LLM and the r…
Learning Refined Document Representations for Dense Retrieval via Deliberate Thinking
Yifan Ji, Zhipeng Xu, Zhenghao Liu +7
Recent dense retrievers increasingly leverage the robust text understanding capabilities of Large Language Models (LLMs), encoding queries and documents into a shared embedding spa…