Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）；Alibaba Cloud（阿里云）；Hong Kong Baptist University（香港浸会大学）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhiyuan Research Institute（智源研究院）；Fabu Inc.（fabu公司）
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 a critical area of research. Traditional methods rely heavily on extensive data for Reinforcement Learning from Human Feedback (RLHF), but representation engineering offers a new, training-free approach. This technique leverages semantic features to control the representation of LLM's intermediate hidden states, enabling the model to meet specific requirements such as increased honesty or heightened safety awareness. However, a significant challenge arises when attempting to fulfill multiple requirements simultaneously. It proves difficult to encode various semantic contents, like honesty and safety, into a singular semantic feature, restricting its practicality. In this work, we address this issue through ``Sparse Activation Control''. By delving into the intrinsic mechanisms of LLMs, we manage to identify and pinpoint components that are closely related to specific tasks within the model, i.e., attention heads. These heads display sparse characteristics that allow for near-independent control over different tasks. Our experiments, conducted on the open-source Llama series models, have yielded encouraging results. The models were able to align with human preferences on issues of safety, factuality, and bias concurrently.
From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports -- with Preliminary Extension to Lung Cancer
Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, China（渝粤病理研究所，金风实验室，重庆，中国）；T Magnetic Resonance Imaging Translational Medical Center, Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, China（7T磁共振成像转化医学中心，放射科，西南医院，中国人民解放军军医大学，重庆，中国）
Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学耿丽人工智能学院）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；Ant Group（蚂蚁集团）
MLA-Trust: Benchmarking Trustworthiness of Multimodal LLM Agents in GUI Environments
Department of Computer Science & Technology, Institute for AI, BNRist Center, THBI Lab, Tsinghua-Bosch Joint Center for ML（计算机科学与技术系、人工智能研究院、BNRist中心、THBI实验室、清华-博世联合机器学习中心）；Shanghai Key Lab. of Multidimensional Info. Processing（上海多维信息处理重点实验室）
A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models
School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences（神经语言处理与机器智能中心，中国科学院自动化研究所）
LF-Steering: Latent Feature Activation Steering for Enhancing Semantic Consistency in Large Language Models
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；IT Innovation and Research Center, Huawei Technologies（华为技术有限公司 IT 创新研究中心）；Informatics, University of Edinburgh（爱丁堡大学信息学院）
Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；National University of Defense Technology（国防科技大学）；Chongqing University of Technology（重庆理工大学）
ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning
Department of Computer Science（计算机科学系）；San Francisco State University（旧金山州立大学）；Department of Mechanical & Industrial Engineering（机械与工业工程系）；Louisiana State University（路易斯安那州立大学）
Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models?
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）
