Summary of Sed: Self-evaluation Decoding Enhances Large Language Models For Better Generation, by Ziqin Luo et al.
SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation
by Ziqin Luo, Haixia Han, Haokun Zhao, Guochao Jiang, Chengyu Du, Tingyun Li, Jiaqing Liang, Deqing Yang, Yanghua Xiao
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 A novel approach, Self-Evaluation Decoding (SED), is proposed to improve Large Language Models’ (LLMs) text generation capabilities by integrating speculation and evaluation steps into the decoding process. This technique mirrors human decision-making, allowing LLMs to make more informed token selection decisions at uncertain points, dubbed “chaotic points.” Experimental results across various tasks using different LLMs demonstrate SED’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are very smart computers that can generate text by responding to user queries. However, they often struggle with certain parts of the text called “chaotic points” where it’s hard to make good choices. This paper introduces a new way for these models to think more carefully about their decisions, making better choices at chaotic points. By doing so, the generated text becomes higher quality and more accurate. |
Keywords
» Artificial intelligence » Text generation » Token
