3 citations · 3 across the 31 of their papers we have counts for
5 papers · 1 filter
LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning
Yansheng Mao, Jiaqi Li, Fanxu Meng +3
Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT) for long context…
Autoregressive Models in Vision: A Survey
Jing Xiong, Gongye Liu, Lun Huang +17
Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in co…
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation
Zixuan Li, Jing Xiong, Fanghua Ye +9
We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that utilizes Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate simil…
UNComp: Can Matrix Entropy Uncover Sparsity? -- A Compressor Design from an Uncertainty-Aware Perspective
Jing Xiong, Jianghan Shen, Fanghua Ye +9
Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands. While techniques such as Key-Value…
D2O: Dynamic Discriminative Operations for Efficient Long-Context Inference of Large Language Models
Zhongwei Wan, Xinjian Wu, Yu Zhang +8
Generative inference in Large Language Models (LLMs) is impeded by the growing memory demands of Key-Value (KV) cache, especially for longer sequences. Traditional KV cache evictio…