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Knowledge editing aims to update outdated information in Large Language Models (LLMs).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…
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…
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Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated.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…