15 citations · 37 across the 11 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
cs.LG2024★ 2 cited
Discovering Preference Optimization Algorithms with and for Large Language Models
Chris Lu, Samuel Holt, Claudio Fanconi +4
Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as…
cs.LG2024★ 2 cited
Dense Reward for Free in Reinforcement Learning from Human Feedback
Alex J. Chan, Hao Sun, Samuel Holt +1
Reinforcement Learning from Human Feedback (RLHF) has been credited as the key advance that has allowed Large Language Models (LLMs) to effectively follow instructions and produce…