The Impact of Reasoning Step Length on Large Language Models
- URL: http://arxiv.org/abs/2401.04925v4
- Date: Sat, 22 Jun 2024 08:18:48 GMT
- Title: The Impact of Reasoning Step Length on Large Language Models
- Authors: Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du,
- Abstract summary: Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models.
We investigate the correlation between the effectiveness of CoT and the length of reasoning steps in prompts.
- Score: 40.546685248243534
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on this, we have conducted several empirical experiments to explore the relations. Specifically, we design experiments that expand and compress the rationale reasoning steps within CoT demonstrations while keeping all other factors constant. We have the following key findings. First, the results indicate that lengthening the reasoning steps in prompts, even without adding new information into the prompt, considerably enhances LLMs' reasoning abilities across multiple datasets. Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models. This finding highlights the importance of the number of steps in CoT prompts and provides practical guidance to make better use of LLMs' potential in complex problem-solving scenarios. Second, we also investigated the relationship between the performance of CoT and the rationales used in demonstrations. Surprisingly, the result shows that even incorrect rationales can yield favorable outcomes if they maintain the requisite length of inference. Third, we observed that the advantages of increasing reasoning steps are task-dependent: simpler tasks require fewer steps, whereas complex tasks gain significantly from longer inference sequences. The code is available at https://github.com/MingyuJ666/The-Impact-of-Reasoning-Step-Length-on-Large-Language-Models
Related papers
- Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models [56.37421741507468]
Chain-of-Thought (CoT) reasoning has significantly enhanced the performance of large language models (LLMs)
We propose a method to identify critical reasoning steps using perplexity as a measure of their importance.
arXiv Detail & Related papers (2025-02-18T20:04:51Z) - When More is Less: Understanding Chain-of-Thought Length in LLMs [53.77747102201451]
Chain-of-thought (CoT) reasoning enhances the multi-step reasoning capabilities of large language models (LLMs)
However, for most models and tasks, does an increase in CoT length consistently lead to improved reasoning accuracy?
In this paper, we observe a nuanced relationship: as the number of reasoning steps increases, performance initially improves but eventually decreases.
arXiv Detail & Related papers (2025-02-11T05:28:59Z) - Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning [73.2950349728376]
Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks.
However, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between pieces of information.
This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering.
We propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context.
arXiv Detail & Related papers (2025-01-14T05:18:20Z) - Markov Chain of Thought for Efficient Mathematical Reasoning [10.678633785012691]
Chain of Thought (CoT) of multi-step benefits from the logical structure of the reasoning steps and task-specific actions.
We conceptualize the standard multi-step CoT as a novel Markov Chain of Thought (MCoT)
arXiv Detail & Related papers (2024-10-23T07:53:29Z) - Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning Distillation [24.272384832200522]
We propose mistaktextbfE-textbfDriven key reasontextbfIng step distillatextbfTion (textbfEDIT)
We design prompts to generate dual CoTs data with similar reasoning paths but divergent conclusions.
Experiments validate the effectiveness of EDIT across both in-domain and out-of-domain benchmark reasoning datasets.
arXiv Detail & Related papers (2024-05-30T06:32:11Z) - Question Decomposition Improves the Faithfulness of Model-Generated
Reasoning [23.34325378824462]
Large language models (LLMs) are difficult to verify the correctness and safety of their behavior.
One approach is to prompt LLMs to externalize their reasoning, by having them generate step-by-step reasoning as they answer a question.
This approach relies on the stated reasoning faithfully reflecting the model's actual reasoning, which is not always the case.
Decomposition-based methods achieve strong performance on question-answering tasks, sometimes approaching that of CoT.
arXiv Detail & Related papers (2023-07-17T00:54:10Z) - Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models [81.01397924280612]
Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations.
We introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts Prompting), an iterative bootstrapping approach for selecting exemplars and generating reasoning chains.
arXiv Detail & Related papers (2023-04-23T13:54:39Z) - Towards Understanding Chain-of-Thought Prompting: An Empirical Study of
What Matters [82.84696222087396]
Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs)
We show that CoT reasoning is possible even with invalid demonstrations.
arXiv Detail & Related papers (2022-12-20T05:20:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.