Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun, Mihaela van der Schaar·December 18, 2024
Summary
The paper introduces Few-shot Steerable Alignment, a method using Neural Processes to adapt rewards and LLM policies for personalized preference functions in datasets with diverse user choices. It addresses the challenge of heterogeneous human preferences by extending the Bradley-Terry-Luce model to handle unobserved variability factors. The framework allows for practical implementations of reward modeling and LLM fine-tuning, capturing diverse human preferences in a data-efficient manner. LLMs trained with this approach can generate outputs over a continuum of behavioral modes, aligning with individual preferences at inference time. The effectiveness of the methods is empirically validated, demonstrating their capability to capture and align with diverse human preferences.
Introduction
Background
Overview of personalized preference functions in AI
Challenges in modeling diverse human preferences
Introduction to the Bradley-Terry-Luce (BTL) model
Objective
To introduce a novel method for adapting rewards and LLM policies for personalized preference functions
To address the challenge of heterogeneous human preferences in datasets with diverse user choices
Method
Data Collection
Description of the dataset used
Methods for collecting diverse user choices
Data Preprocessing
Techniques for handling unobserved variability factors
Preprocessing steps for preparing data for Neural Processes
Neural Processes
Explanation of Neural Processes
How Neural Processes are utilized in the context of Few-shot Steerable Alignment
Reward Modeling
Techniques for modeling rewards in the context of personalized preferences
Integration of reward modeling with Neural Processes
LLM Fine-tuning
Methods for fine-tuning LLMs with personalized preference functions
Alignment of LLMs with individual preferences
Capturing Diverse Preferences
Strategies for capturing diverse human preferences in a data-efficient manner
Implementation of the framework for practical reward modeling and LLM fine-tuning
Empirical Validation
Methodology
Description of the experimental setup
Metrics for evaluating the effectiveness of Few-shot Steerable Alignment
Results
Empirical demonstration of the method's capability to capture and align with diverse human preferences
Comparison with baseline methods
Conclusion
Summary of findings
Implications for future research and practical applications
Basic info
papers
machine learning
artificial intelligence
Advanced features