The paper introduces Approximate Pareto Optimality (APO) to address the challenge of aligning large language models (LLMs) with user preferences under data scarcity. APO groups users with compatible updates, combines gradient descent with controlled ascent to handle competing objectives, and iteratively refines initializations for better personalization. Experiments on Fed-ChatbotPA and UltraFeedback demonstrate improvements using only 20 local examples.
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This approach enables more effective personalization of LLM responses with limited user data by grouping compatible users and optimizing for diverse preferences through collaborative learning.
This survey explores post-training and alignment techniques in video generation models, highlighting challenges like temporal coherence and physical constraints. It categorizes methods into supervised fine-tuning, self-training, preference-based approaches, and inference-time strategies, while discussing datasets, evaluation practices, and open challenges.
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5 days agoLessWrongThe text discusses a scenario where an AI must respond to the question 'What's the date?' without access to real-time data. It explores the challenge of providing an accurate date without hallucinating, considering OpenAI's training focus on avoiding false information. The text speculates on potential model versions and knowledge cutoff dates but acknowledges uncertainty due to lack of explicit information.
5 days ago