Summary of To Cot or Not to Cot? Chain-of-thought Helps Mainly on Math and Symbolic Reasoning, by Zayne Sprague et al.
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
by Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, Greg Durrett
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 investigates the effectiveness of chain-of-thought (CoT) prompting in large language models (LLMs) for various tasks. By conducting a meta-analysis of over 100 papers and running evaluations on 20 datasets across 14 models, researchers found that CoT primarily benefits tasks involving math or logic, with smaller gains on other types. The study also compares the performance of CoT against directly generating answers without prompting and tool-augmented LLMs. Results show that much of CoT’s gain comes from improving symbolic execution, but it underperforms compared to using a symbolic solver. The findings suggest applying CoT selectively to maintain performance while reducing inference costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models (LLMs) can think and reason when given extra “thinking” prompts. Researchers studied over 100 papers and ran their own tests on 20 datasets with 14 different LLMs. They found that these thinking prompts mostly help with math and logic problems, but not as much for other types of tasks. The study also compared the performance of using prompts to directly generating answers without them. Results show that while using prompts can improve performance, it’s not always the best approach. This research suggests that we should be more strategic about when we use these thinking prompts. |
Keywords
» Artificial intelligence » Inference » Prompting
