Summary of Generating Chain-of-thoughts with a Pairwise-comparison Approach to Searching For the Most Promising Intermediate Thought, by Zhen-yu Zhang et al.
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
by Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel method to improve large language models’ ability to tackle complex reasoning problems by guiding them through step-by-step problem solving. The chain-of-thoughts (CoT) approach involves generating intermediate thoughts and evaluating them using the LLM, which guides subsequent thought generation. However, current methods rely on noisy and unreliable evaluation from the LLM, potentially misguiding the process. This paper addresses this issue by introducing pairwise-comparison evaluation, where the LLM is prompted to select the more promising intermediate thought from each pair. To further mitigate noise, the authors incorporate techniques from ensemble learning and dueling bandits, proposing two algorithm variants. Experimental results on three real-world tasks demonstrate the effectiveness of these proposed algorithms, highlighting the significance of pairwise comparison in the CoT framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to solve a puzzle step by step, but you’re not sure which step to take next. That’s where large language models (LLMs) usually struggle. This paper suggests a new way to help LLMs think more clearly and logically by breaking down complex problems into smaller, manageable steps. The problem is that current methods for doing this are flawed because they rely on the model’s own judgment, which can be unreliable. This paper proposes a new approach called “pairwise comparison” that helps the model make better decisions by comparing different options and choosing the best one. The authors tested their idea on three real-world tasks and found it worked much better than previous methods. |
