1 citations · 1 across the 5 of their papers we have counts for
5 papers
Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancement
Xiaowei Yuan, Zhao Yang, Ziyang Huang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect context…
Probabilistic Uncertain Reward Model
Wangtao Sun, Xiang Cheng, Xing Yu +5
Reinforcement learning from human feedback (RLHF) is a critical technique for training large language models. However, conventional reward models based on the Bradley-Terry model (…
Improving Zero-shot LLM Re-Ranker with Risk Minimization
Xiaowei Yuan, Zhao Yang, Yequan Wang +2
In the Retrieval-Augmented Generation (RAG) system, advanced Large Language Models (LLMs) have emerged as effective Query Likelihood Models (QLMs) in an unsupervised way, which re-…
Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint
Xiaowei Yuan, Zhao Yang, Yequan Wang +3
Large language models internalize enormous parametric knowledge during pre-training. Concurrently, realistic applications necessitate external contextual knowledge to aid models on…
Logic Traps in Evaluating Attribution Scores
Yiming Ju, Yuanzhe Zhang, Zhao Yang +3
Modern deep learning models are notoriously opaque, which has motivated the development of methods for interpreting how deep models predict. This goal is usually approached with at…