Summary of Enhancing Zero-shot Chain Of Thought Prompting Via Uncertainty-guided Strategy Selection, by Shanu Kumar et al.
Enhancing Zero-shot Chain of Thought Prompting via Uncertainty-Guided Strategy Selection
by Shanu Kumar, Saish Mendke, Karody Lubna Abdul Rahman, Santosh Kurasa, Parag Agrawal, Sandipan Dandapat
First submitted to arxiv on: 30 Nov 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 This paper proposes a novel approach to chain-of-thought (CoT) prompting for large language models (LLMs), addressing limitations in current methods. The Zero-shot Uncertainty-based Selection (ZEUS) method leverages uncertainty estimates to select effective demonstrations without requiring access to model parameters, enhancing the precision and reliability of CoT prompting. ZEUS shows consistent outperformance across four challenging reasoning benchmarks, demonstrating its robustness and scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoT prompting helps big language models reason better. But current methods have flaws: one needs expert help to create examples, or uses special words that can be wrong. This paper suggests a new way called ZEUS (Zero-shot Uncertainty-based Selection). It chooses good demonstrations without knowing the model’s inner workings. This is important because it makes CoT prompting more precise and reliable. |
Keywords
» Artificial intelligence » Precision » Prompting » Zero shot
