Summary of Personalized Adaptation Via In-context Preference Learning, by Allison Lau et al.
Personalized Adaptation via In-Context Preference Learning
by Allison Lau, Younwoo Choi, Vahid Balazadeh, Keertana Chidambaram, Vasilis Syrgkanis, Rahul G. Krishnan
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Preference Pretrained Transformer (PPT) is a novel approach for adaptive personalization using online user feedback in Reinforcement Learning from Human Feedback (RLHF) to align Language Models (LMs) with human preferences. The PPT consists of two phases: an offline phase where a single policy model is trained using a history-dependent loss function, and an online phase where the model adapts to individual user preferences through in-context learning. This approach achieves personalized adaptation superior to existing methods while significantly reducing computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RLHF helps language models match human preferences by using feedback from users. However, this method often ignores what each person likes specifically, resulting in subpar personalization. Researchers developed the Preference Pretrained Transformer (PPT) to improve personalization for individual users. The PPT is a two-part system that first learns based on past user interactions and then adjusts its results according to current user preferences. This new approach can personalize language models better than existing methods while using less computer power. |
Keywords
» Artificial intelligence » Loss function » Reinforcement learning from human feedback » Rlhf » Transformer
