4 citations · 5 across the 5 of their papers we have counts for
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cs.LG2024★ 4 cited
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.…
cs.LG2024★ 1 cited
Multi-objective Reinforcement learning from AI Feedback
Marcus Williams
This paper presents Multi-Objective Reinforcement Learning from AI Feedback (MORLAIF), a novel approach to improving the alignment and performance of language models trained using…