More capable models can better recognize the specific circumstances under which they are trained. Because of this, they are more likely to learn to act as expected in precisely those circumstances while behaving competently but unexpectedly in others. This can surface in the form of problems that Perez et al. (2022) call sycophancy, where a model answers subjective questions in a way that flatters their user’s stated beliefs, and sandbagging, where models are more likely to endorse common misconceptions when their user appears to be less educated.
Recent articles
- Phoenix.new is Fly's entry into the prompt-driven app development space - 23rd June 2025
- Trying out the new Gemini 2.5 model family - 17th June 2025
- The lethal trifecta for AI agents: private data, untrusted content, and external communication - 16th June 2025