36 citations · 48 across the 8 of their papers we have counts for
5 papers · 1 filter
IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neurons
Dan Shi, Renren Jin, Tianhao Shen +3
It is widely acknowledged that large language models (LLMs) encode a vast reservoir of knowledge after being trained on mass data. Recent studies disclose knowledge conflicts in LL…
ConTrans: Weak-to-Strong Alignment Engineering via Concept Transplantation
Weilong Dong, Xinwei Wu, Renren Jin +2
Ensuring large language models (LLM) behave consistently with human goals, values, and intentions is crucial for their safety but yet computationally expensive. To reduce the compu…
Exploring Multilingual Concepts of Human Value in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages?
Shaoyang Xu, Weilong Dong, Zishan Guo +2
Prior research has revealed that certain abstract concepts are linearly represented as directions in the representation space of LLMs, predominantly centered around English. In thi…
Large Language Model Alignment: A Survey
Tianhao Shen, Renren Jin, Yufei Huang +6
Recent years have witnessed remarkable progress made in large language models (LLMs). Such advancements, while garnering significant attention, have concurrently elicited various c…
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks
Weilong Dong, Xinwei Wu, Junzhuo Li +3
Massively multi-task learning with large language models has recently made substantial progress on few-shot generalization. However, this is usually performed in a centralized lear…