Summary of Think Beyond Size: Adaptive Prompting For More Effective Reasoning, by Kamesh R
Think Beyond Size: Adaptive Prompting for More Effective Reasoning
by Kamesh R
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 introduces Adaptive Prompting, a dynamic and iterative framework for enhancing multi-step reasoning in natural language processing (NLP) tasks. Building upon recent techniques like chain-of-thought prompting, this approach dynamically adjusts prompt structures and incorporates real-time validation to improve performance on complex reasoning benchmarks. The authors demonstrate significant accuracy gains on diverse reasoning tasks, including arithmetic, logical, and commonsense tasks, without requiring fine-tuning or task-specific training data. By leveraging guided prompts, intermediate validation, and self-corrective steps, Adaptive Prompting enables smaller models to achieve competitive performance with larger counterparts while maintaining computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how computers can get better at solving complex problems by giving them hints in a special order. This “adaptive prompting” trick lets small computer models do big tasks like arithmetic and logical reasoning as well as bigger models, but using less computer power. The authors tested this idea on many different types of problems and found it worked really well, even without needing extra training or data. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Nlp » Prompt » Prompting
