10 citations · 15 across the 7 of their papers we have counts for
5 papers · 1 filter
FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users
Anikait Singh, Sheryl Hsu, Kyle Hsu +5
Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context…
Test-Time Alignment via Hypothesis Reweighting
Yoonho Lee, Jonathan Williams, Henrik Marklund +4
Reward models trained on aggregate preferences often fail to capture individual users' values, but existing adaptation methods such as fine-tuning or long-context conditioning are…
Calibrating Language Models with Adaptive Temperature Scaling
Johnathan Xie, Annie S. Chen, Yoonho Lee +2
The effectiveness of large language models (LLMs) is not only measured by their ability to generate accurate outputs but also by their calibration-how well their confidence scores…
A Critical Evaluation of AI Feedback for Aligning Large Language Models
Archit Sharma, Sedrick Keh, Eric Mitchell +3
Reinforcement learning with AI feedback (RLAIF) is a popular paradigm for improving the instruction-following abilities of powerful pre-trained language models. RLAIF first perform…
RLVF: Learning from Verbal Feedback without Overgeneralization
Moritz Stephan, Alexander Khazatsky, Eric Mitchell +4
The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements…