6 citations · 14 across the 13 of their papers we have counts for
5 papers · 1 filter
CMT: A Memory Compression Method for Continual Knowledge Learning of Large Language Models
Dongfang Li, Zetian Sun, Xinshuo Hu +2
Large Language Models (LLMs) need to adapt to the continuous changes in data, tasks, and user preferences. Due to their massive size and the high costs associated with training, LL…
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG
Xinping Zhao, Yan Zhong, Zetian Sun +5
Retrieval-Augmented Generation (RAG) prevails in Large Language Models. It mainly consists of retrieval and generation. The retrieval modules (a.k.a. retrievers) aim to find useful…
In-Context Learning State Vector with Inner and Momentum Optimization
Dongfang Li, Zhenyu Liu, Xinshuo Hu +3
Large Language Models (LLMs) have exhibited an impressive ability to perform In-Context Learning (ICL) from only a few examples. Recent works have indicated that the functions lear…
Improving Attributed Text Generation of Large Language Models via Preference Learning
Dongfang Li, Zetian Sun, Baotian Hu +4
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content. Recent works aim to reduce misinformati…
Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer
Xinshuo Hu, Baotian Hu, Dongfang Li +2
The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of a…