Reinforcement learning from human feedback (RLHF) is a popular technique for training high-quality AI assistants. However, RLHF may also encourage model responses that match user beliefs over truthful responses, a behavior known as sycophancy. We investigate the prevalence of sycophancy in RLHF-trained models and whether human preference judgments are responsible. We first demonstrate that five state-of-the-art AI assistants consistently exhibit sycophancy behavior across four varied free-form text-generation tasks. To understand if human preferences drive this broadly observed behavior of RLHF models, we analyze existing human preference data. We find that when a response matches a user's views, it is more likely to be preferred. Moreover, both humans and preference models (PMs) prefer convincingly-written sycophantic responses over correct ones a non-negligible fraction of the time. Optimizing model outputs against PMs also sometimes sacrifices truthfulness in favor of sycophancy. Overall, our results indicate that sycophancy is a general behavior of RLHF models, likely driven in part by human preference judgments favoring sycophantic responses.

We investigate a family of simple long-context attacks on large language models: prompting with hundreds of demonstrations of undesirable behavior. This attack is newly feasible with the larger context windows recently deployed by language model providers like Google DeepMind, OpenAI and Anthropic. We find that in diverse, realistic circumstances, the effectiveness of this attack follows a power law, up to hundreds of shots. We demonstrate the success of this attack on the most widely used state-of-the-art closed-weight models, and across various tasks. Our results suggest very long contexts present a rich new attack surface for LLMs.

This paper describes the design, fabrication, and test results of the conversion of a General Motors Suburban into a hybrid electric vehicle (HEV). A parallel drive train configuration is utilized with an internal combustion engine running on reformulated gasoline and two electric motors. The overall system is monitored and controlled by a real-time control system. One objective in the design was to minimize changes in the Suburban. design decisions, expected results and actual results are presented.

This paper describes the development of a proton exchange membrane (PEM) fuel cell powered vehicle. The vehicle is a converted Suburban with a series hybrid electric configuration. The auxiliary power unit is a PEM fuel cell that is used to power the vehicle and charge the batteries when cruising. System requirements and design decisions are presented.