Summary of Autoreason: Automatic Few-shot Reasoning Decomposition, by Arda Sevinc and Abdurrahman Gumus
AutoReason: Automatic Few-Shot Reasoning Decomposition
by Arda Sevinc, Abdurrahman Gumus
First submitted to arxiv on: 9 Dec 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 The Chain of Thought (CoT) method was designed to enhance step-by-step reasoning in Large Language Models (LLMs), but it has some limitations. CoT requires hand-crafted few-shot exemplar prompts, which can be time-consuming and restrictive. Additionally, CoT is not capable of adjusting itself to different queries, making it less versatile than other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have been designed to improve step-by-step reasoning using the Chain of Thought method. However, this method has limitations, requiring hand-crafted prompts and unable to adjust to different questions. |
Keywords
» Artificial intelligence » Few shot
