Summary of Infalign: Inference-aware Language Model Alignment, by Ananth Balashankar and Ziteng Sun and Jonathan Berant and Jacob Eisenstein and Michael Collins and Adrian Hutter and Jong Lee and Chirag Nagpal and Flavien Prost and Aradhana Sinha and Ananda Theertha Suresh and Ahmad Beirami
InfAlign: Inference-aware language model alignment
by Ananth Balashankar, Ziteng Sun, Jonathan Berant, Jacob Eisenstein, Michael Collins, Adrian Hutter, Jong Lee, Chirag Nagpal, Flavien Prost, Aradhana Sinha, Ananda Theertha Suresh, Ahmad Beirami
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Theory (cs.IT)
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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 paper addresses the challenge of aligning language models for improved performance in generative tasks. The authors highlight the limitations of traditional reinforcement learning-based methods when used with inference-time decoding algorithms, such as Best-of-N and controlled decoding. To overcome this limitation, they propose a novel framework called InfAlign, which optimizes the alignment policy to improve inference-time win rates against a base model. The framework involves a reward calibration step and a KL-regularized reward maximization step with a transformation of the calibrated reward. The authors demonstrate the effectiveness of their approach by proposing specific transformations for Best-of-N sampling and jailbreaking, achieving up to 3-8% improvement in inference-time win rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models work better together. Right now, we’re using special algorithms to decode what language models say, rather than just picking random words. But this makes it hard for the model to learn how to be good at generating text. To fix this, the authors propose a new way of aligning language models so they can work well with these decoding algorithms. They show that their approach can improve performance by up to 3-8%. This is important because it will help make language models better at doing things like writing stories and composing music. |
Keywords
» Artificial intelligence » Alignment » Inference » Reinforcement learning
