Summary of Supervised Chain Of Thought, by Xiang Zhang et al.
Supervised Chain of Thought
by Xiang Zhang, Dujian Ding
First submitted to arxiv on: 18 Oct 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 paper explores the limitations of Large Language Models (LLMs) in solving complex reasoning tasks due to their inherent limitations in computational depth. Chain of Thought (CoT) prompting has shown promise in addressing these limitations, but its “one-prompt-for-all” approach can negatively impact performance. The authors build upon previous theoretical analyses to demonstrate that task-specific supervision is crucial for navigating the prompt space accurately and achieving optimal performance. They partition the solution search space into two: the prompt space and the answer space, revealing a gap in reasoning performance when supervision is applied versus when it is not. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have made huge progress in natural language processing, but they’re limited by their architecture. This paper looks at how to make them better for complex thinking tasks. Right now, these models use one way of asking questions that works for many different problems, but this can actually make it harder for the model to give good answers. The authors find that giving the model specific guidance for each task helps a lot and can close the gap between what they can do with or without supervision. |
Keywords
» Artificial intelligence » Natural language processing » Prompt » Prompting
