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cs.LG2026
Predicting LLM Safety Before Release by Simulating Deployment
Marcus Williams, Hannah Sheahan, Cameron Raymond +8
Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model. Yet most evaluations provide limited evidence about how often undesired model beha…
cs.LG2025
On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback
Marcus Williams, Micah Carroll, Adhyyan Narang +3
As LLMs become more widely deployed, there is increasing interest in directly optimizing for feedback from end users (e.g. thumbs up) in addition to feedback from paid annotators.…