1 citations · 1 across the 5 of their papers we have counts for
5 papers
Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs
Haowen Pan, Xiaozhi Wang, Yixin Cao +4
Knowledge editing aims to update outdated information in Large Language Models (LLMs). A representative line of study is locate-then-edit methods, which typically employ causal tra…
PairJudge RM: Perform Best-of-N Sampling with Knockout Tournament
Yantao Liu, Zijun Yao, Rui Min +3
Best-of-N (BoN) sampling, a common strategy for test-time scaling of Large Language Models (LLMs), relies on reward models to select the best candidate solution from multiple gener…
RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style
Yantao Liu, Zijun Yao, Rui Min +3
Reward models are critical in techniques like Reinforcement Learning from Human Feedback (RLHF) and Inference Scaling Laws, where they guide language model alignment and select opt…
Event-level Knowledge Editing
Hao Peng, Xiaozhi Wang, Chunyang Li +5
Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated. Existing work edits LLMs at the level of factual knowledge trip…
Finding and Editing Multi-Modal Neurons in Pre-Trained Transformers
Haowen Pan, Yixin Cao, Xiaozhi Wang +2
Understanding the internal mechanisms by which multi-modal large language models (LLMs) interpret different modalities and integrate cross-modal representations is becoming increas…