Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involves employing reinforcement learning from human feedback (RLHF) to fine-tune LLMs based on human labels assessing the relative quality of model responses. Nevertheless, RLHF is susceptible to instability during fine-tuning and presents challenges in implementation.Drawing inspiration from the emerging field of representation engineering (RepE), this study aims to identify relevant representations for high-level human preferences embedded in patterns of activity within an LLM, and achieve precise control of model behavior by transforming its representations. This novel approach, denoted as Representation Alignment from Human Feedback (RAHF), proves to be effective, computationally efficient, and easy to implement.Extensive experiments demonstrate the efficacy of RAHF in not only capturing but also manipulating representations to align with a broad spectrum of human preferences or values, rather than being confined to a singular concept or function (e.g. honesty or bias). RAHF's versatility in accommodating diverse human preferences shows its potential for advancing LLM performance.
Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes
Antiquus S. Hippocampus, Natalia Cerebro & Amelie P. Amygdale Department of Computer Science Cranberry-Lemon University Pittsburgh, PA 15213, USA（计算机科学系， Cranberry-Lemon 大学）；Ji Q. Ren & Yevgeny LeNet Department of Computational Neuroscience University of the Witwatersrand Joburg, South Africa（计算神经科学系， 威特沃特斯兰大学）
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
Microsoft Corporation（微软公司）；Purdue University（普渡大学）；Texas A&M University（德克萨斯农工大学）；University of California San Diego（加州大学圣地亚哥分校）；University of Southern California（南加州大学）
Aligning Language Models Using Follow-up Likelihood as Reward Signal
National University of Singapore（新加坡国立大学）；Peking University（北京大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Tencent AI Lab（腾讯AI实验室）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；University of Science and Technology of China（中国科学技术大学）
Evaluating and Aligning CodeLLMs on Human Preference
Alibaba Group（阿里巴巴集团）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）
TPO: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees
School of Computer Science, Peking University（北京大学计算机科学系）；Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；National Research and Engineering Center of Software Engineering, Peking University（北京大学软件工程研究中心）
