Summary of Energy-based Preference Model Offers Better Offline Alignment Than the Bradley-terry Preference Model, by Yuzhong Hong et al.
Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model
by Yuzhong Hong, Hanshan Zhang, Junwei Bao, Hongfei Jiang, Yang Song
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle a crucial issue in aligning large language models (LLMs) with human preferences using the KL-constrained reinforcement learning from human feedback (RLHF) loss. The authors show that aligning an LLM with human preferences is equivalent to a reward modeling task, where the goal is to find a linear relationship between the true reward and the LLM’s output. However, they identify a significant problem: the RLHF loss may have multiple minimizers, not all of which satisfy this linearity condition. To address this issue, the authors propose an energy-based model (EBM) that inherently satisfies the linearity requirement. They also introduce a contrastive loss called Energy Preference Alignment (EPA), which approximates the EBM’s maximum likelihood estimator (MLE). Theoretical analysis shows that EPA’s approximation error almost surely vanishes when using sufficient negatives, and empirical results demonstrate its superiority over DPO on open benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in aligning large language models with human preferences. It shows that aligning an LLM is like finding the right reward model. But there’s a catch: the way we currently do this might not always give us the best result. The authors propose a new way to solve this problem, called energy-based modeling (EBM). They also introduce a special kind of loss function called Energy Preference Alignment (EPA) that helps us find the right solution. By using EPA and EBM together, we can get better results than before. |
Keywords
» Artificial intelligence » Alignment » Contrastive loss » Energy based model » Likelihood » Loss function » Reinforcement learning from human feedback » Rlhf
