3 papers
cs.LG2025
Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMs
Rui Dai, Sile Hu, Xu Shen +3
Task arithmetic is a straightforward yet highly effective strategy for model merging, enabling the resultant model to exhibit multi-task capabilities. Recent research indicates tha…
cs.CL2025
From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
Wei Chen, Zhen Huang, Liang Xie +9
Large Language Models (LLMs) tend to prioritize adherence to user prompts over providing veracious responses, leading to the sycophancy issue. When challenged by users, LLMs tend t…
cs.CL2024
Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control
Yuxin Xiao, Chaoqun Wan, Yonggang Zhang +5
As the development and application of Large Language Models (LLMs) continue to advance rapidly, enhancing their trustworthiness and aligning them with human preferences has become…