Summary of Few-shot Steerable Alignment: Adapting Rewards and Llm Policies with Neural Processes, by Katarzyna Kobalczyk et al.
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
by Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun, Mihaela van der Schaar
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This novel framework for few-shot steerable alignment tackles the challenge of aligning large language models (LLMs) with diverse user preferences. The current approaches assume homogeneous objectives and rely on single-objective fine-tuning, which is insufficient given human preferences are heterogeneous and influenced by unobservable factors. To address this issue, the authors propose a framework that extends the Bradley-Terry-Luce model to handle heterogeneous preferences with unobserved variability factors. This approach enables LLMs trained with their framework to be adapted to individual preferences at inference time, generating outputs over a continuum of behavioural modes. The proposed method is evaluated empirically and demonstrates its ability to capture and align with diverse human preferences in a data-efficient manner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make large language models more personalized for people’s unique preferences. Right now, these models are mostly used in the same way for everyone, but what if we could make them adapt to each person’s individual tastes? The researchers created a new method that can figure out people’s underlying preferences from just a few examples of their choices. This means we can fine-tune language models to be more tailored to each person’s preferences, making them more useful and user-friendly. |
Keywords
» Artificial intelligence » Alignment » Few shot » Fine tuning » Inference
