In day-to-day communication, people often approximate the truth - for example, rounding the time or omitting details - in order to be maximally helpful to the listener. How do large language models (LLMs) handle such nuanced trade-offs? To address this question, we use psychological models and experiments designed to characterize human behavior to analyze LLMs. We test a range of LLMs and explore how optimization for human preferences or inference-time reasoning affects these trade-offs. We find that reinforcement learning from human feedback improves both honesty and helpfulness, while chain-of-thought prompting skews LLMs towards helpfulness over honesty. Finally, GPT-4 Turbo demonstrates human-like response patterns including sensitivity to the conversational framing and listener's decision context. Our findings reveal the conversational values internalized by LLMs and suggest that even these abstract values can, to a degree, be steered by zero-shot prompting.
HonestLLM: Toward an Honest and Helpful Large Language Model
MBZUAI（Mohamed bin Zayed 人工智能大学）；Huazhong University of Science and Technology（华中科技大学）；University of Notre Dame（圣母大学）；University of Washington（华盛顿大学）；Peking University（北京大学）；Lehigh University（理海大学）
BeHonest: Benchmarking Honesty in Large Language Models
Shanghai Jiao Tong University（上海交通大学）；Carnegie Mellon University（卡内基梅隆大学）；Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；Generative AI Research Lab (GAIR)（生成式人工智能研究实验室（GAIR））
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Tencent Inc.（腾讯公司）
Crisis-Bench: Benchmarking Strategic Ambiguity and Reputation Management in Large Language Models
Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Baptist University（香港 Baptist 大学）；National University of Singapore（新加坡国立大学）；Imperial College London（伦敦帝国理工学院）
RogueAI: A Reverse Turing Test for Detecting Licensed AI Deception in Dialogue
AILab, MIGe, University of Trieste（的里雅斯特大学）；Computational Statistics and Machine Learning, Istituto Italiano di Tecnologia（意大利理工学院）；DIA, University of Trieste（的里雅斯特大学）
LLM-Generated Black-box Explanations Can Be Adversarially Helpful
University of Toronto（多伦多大学）；Vector Institute（矢量研究所）；UMass Amherst（马萨诸塞大学阿默斯特分校）；Stevens Institute of Technology（史蒂文斯理工学院）；Dalhousie University（达尔豪斯大学）
