Summary of Do Llms Really Think Step-by-step in Implicit Reasoning?, by Yijiong Yu
Do LLMs Really Think Step-by-step In Implicit Reasoning?
by Yijiong Yu
First submitted to arxiv on: 24 Nov 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 This research paper investigates whether implicit Chain-of-Thought (CoT) can match the performance of explicit CoT on complex tasks while maintaining faster inference speeds and lower computational costs. The study uses experiments to probe the intermediate steps of language models’ hidden states when trained or prompted for implicit CoT. The results show that when prompted, LLMs rely heavily on experience rather than strict step-by-step reasoning, but when trained, they do calculate intermediate steps. The effect of using implicit CoT is also found to be dependent on the problem format, highlighting its limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores whether a shortcut method called implicit Chain-of-Thought (CoT) can replace traditional explicit CoT methods for language models. Researchers tested this by looking at the “thought process” of language models when they were either trained or prompted to use implicit CoT. They found that when prompted, the models didn’t really think about each step in detail, but rather relied on their past experiences. However, when the models were trained using implicit CoT, they did go through a step-by-step thinking process. The study also shows that the results of using implicit CoT depend on the type of problem being solved. |
Keywords
» Artificial intelligence » Inference
