Summary of Cascade Reward Sampling For Efficient Decoding-time Alignment, by Bolian Li et al.
Cascade Reward Sampling for Efficient Decoding-Time Alignment
by Bolian Li, Yifan Wang, Ananth Grama, Ruqi Zhang
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 proposes a new approach called Cascade Reward Sampling (CARDS) for generating high-reward and high-likelihood text with large language models (LLMs), while reducing computational costs. The method uses rejection sampling to iteratively generate small semantic segments, forming prefixes that guarantee desirable texts. The segment length is dynamically determined by the predictive uncertainty of LLMs. This strategy ensures efficient text generation and alignment with human preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need to be aligned with human preferences for deployment. A technique called decoding-time alignment has shown promise but still struggles to generate high-reward text while keeping costs low. CARDS is a new approach that solves this problem by generating small semantic segments, forming prefixes that ensure desirable texts. This method uses rejection sampling and determines segment length based on the predictive uncertainty of LLMs. |
Keywords
» Artificial intelligence » Alignment » Likelihood » Text generation
