7 citations · 8 across the 10 of their papers we have counts for
10 papers
Normalized Rewards for Preference Optimization
Shawn Im, Federico Danieli, Skyler Seto +2
Direct Alignment Algorithms (DAAs) such as DPO have become a common way to post-train and align LLMs with human preferences. However, DAAs have been observed to over-optimize their…
Aligning LLMs by Predicting Preferences from User Writing Samples
Stéphane Aroca-Ouellette, Natalie Mackraz, Barry-John Theobald +1
Accommodating human preferences is essential for creating aligned LLM agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs acti…
Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models
Anirudh Sundar, Sinead Williamson, Katherine Metcalf +3
Aligned representations across languages is a desired property in multilingual large language models (mLLMs), as alignment can improve performance in cross-lingual tasks. Typically…
ExpertLens: Activation steering features are highly interpretable
Masha Fedzechkina, Eleonora Gualdoni, Sinead Williamson +3
Activation steering methods in large language models (LLMs) have emerged as an effective way to perform targeted updates to enhance generated language without requiring large amoun…
On the Way to LLM Personalization: Learning to Remember User Conversations
Lucie Charlotte Magister, Katherine Metcalf, Yizhe Zhang +1
Large Language Models (LLMs) have quickly become an invaluable assistant for a variety of tasks. However, their effectiveness is constrained by their ability to tailor responses to…
PREDICT: Preference Reasoning by Evaluating Decomposed preferences Inferred from Candidate Trajectories
Stephane Aroca-Ouellette, Natalie Mackraz, Barry-John Theobald +1
Accommodating human preferences is essential for creating AI agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs to infer pref…
On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization
Yong Lin, Skyler Seto, Maartje ter Hoeve +6
Reinforcement Learning from Human Feedback (RLHF) is an effective approach for aligning language models to human preferences. Central to RLHF is learning a reward function for scor…
Hindsight PRIORs for Reward Learning from Human Preferences
Mudit Verma, Katherine Metcalf
Preference based Reinforcement Learning (PbRL) removes the need to hand specify a reward function by learning a reward from preference feedback over policy behaviors. Current appro…
Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards
Katherine Metcalf, Miguel Sarabia, Natalie Mackraz +1
Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that…
Large Language Models as Generalizable Policies for Embodied Tasks
Andrew Szot, Max Schwarzer, Harsh Agrawal +6
We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Po…