Summary of The Impact Of Reasoning Step Length on Large Language Models, by Mingyu Jin et al.
The Impact of Reasoning Step Length on Large Language Models
by Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du
First submitted to arxiv on: 10 Jan 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 The paper explores the impact of reasoning step length on large language models’ (LLMs) abilities in complex problem-solving scenarios. The researchers design experiments that manipulate the number of reasoning steps within Chain of Thought (CoT) demonstrations while keeping other factors constant. They find that increasing the reasoning step length enhances LLMs’ reasoning abilities across multiple datasets, but shortening the steps diminishes their performance. This highlights the importance of reasoning step length in CoT prompts and provides practical guidance for leveraging LLMs’ potential. The study also reveals a task-dependent relationship between reasoning step length and performance, with simpler tasks requiring fewer steps and complex tasks benefiting from longer inference sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well large language models can reason when given different lengths of instructions to follow. They find that giving the models more steps to work through makes them better at solving problems, but taking away those steps makes them worse. This is important because it shows that the length of the instructions really matters, and we need to pay attention to this if we want to get the most out of these language models. |
Keywords
* Artificial intelligence * Attention * Inference
