Summary of Rethinking Chain-of-thought From the Perspective Of Self-training, by Zongqian Wu et al.
Rethinking Chain-of-Thought from the Perspective of Self-Training
by Zongqian Wu, Baoduo Xu, Ruochen Cui, Mengmeng Zhan, Xiaofeng Zhu, Lei Feng
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Chain-of-thought (CoT) framework to improve reasoning performance in Large Language Models (LLMs). By leveraging model-generated information, CoT reasoning has shown great potential. The authors note that both CoT and self-training aim to reduce prediction uncertainty through iterative refinement. Their framework combines two key modules: a task-specific prompt module for initial reasoning and an adaptive reasoning iteration module to address limitations like over-reasoning and high similarity between iterations. Experimental results demonstrate significant improvements in performance and computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help large language models think more clearly. It’s called Chain-of-thought, or CoT. The idea is that by using the model’s own thoughts, you can make it better at understanding and answering questions. The researchers found that both CoT and self-training have the same goal: to get rid of uncertainty in predictions. They came up with a new way to do this, combining two parts: one for starting the thinking process and another for making adjustments along the way. This new approach did really well in tests, making it more efficient too. |
Keywords
» Artificial intelligence » Prompt » Self training
