Hello everyone. This is Li, a research student.
I would like to introduce a paper I read at the recent English seminar, along with my thoughts on it.

Paper Title: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Publication Year: 2022
Authors: Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou
Journal: Advances in neural information processing systems
Volume: Vol. 35
Pages: 24824-24837

This paper examines methods for enabling Large Language Models (LLMs) to effectively process complex tasks. I believe that learning the methods introduced in this paper can help us efficiently utilize LLM applications (such as ChatGPT) for our research, so I wanted to recommend this interesting and practical paper to everyone. The content of the paper is summarized below.

In recent years, with the rapid development of Large Language Models (LLMs), their fundamental capabilities have improved significantly due to the scaling of model size. However, for tasks requiring multi-step complex reasoning, such as arithmetic, common sense understanding, and symbolic reasoning, traditional prompting methods still have limitations. To address this challenge, this paper proposes a new method called “Chain-of-Thought Prompting (CoT).” This method significantly improves the model’s reasoning capabilities by having it generate a series of intermediate reasoning steps.

Chain-of-Thought (CoT) is a method where, for an input problem, the model generates a clear, logical, and consistent sequence of intermediate steps, forming a complete reasoning process. This method mimics the natural reasoning process humans use when dealing with complex problems, and by explicitly providing examples of specific reasoning procedures, it aims to make it easier for the model to understand and execute multi-step reasoning tasks. For example, for a math problem like “Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?”, a traditional method might simply answer “11,” but with CoT, it provides a detailed reasoning process: “Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.”

This paper verifies the effectiveness of CoT through multiple experiments. In arithmetic reasoning tasks, using the PaLM 540B model and providing only 8 CoT examples, it achieved the then-state-of-the-art performance on the GSM8K math problem benchmark, significantly outperforming traditional prompting methods. Furthermore, CoT also demonstrates excellent performance in common sense reasoning (e.g., CommonsenseQA, StrategyQA). Specifically, the CommonsenseQA task requires reasoning that integrates real-world knowledge, such as “whether a pear sinks when put in water.” Additionally, the StrategyQA task requires multi-step strategic reasoning to judge the validity of specific actions. Moreover, in symbolic reasoning tasks (e.g., letter concatenation or coin flip reasoning), CoT shows excellent generalization capabilities even for longer inputs that the model has not encountered before.

In the conclusion of this paper, it is stated that the Chain-of-Thought method is simple, efficient, and requires no additional training, and that it can significantly improve the reasoning performance of LLMs with just a few natural language examples. This capability gradually becomes apparent as the model scale increases, contributing to improved complex task processing. On the other hand, the authors point out that while CoT provides explainability for the reasoning process, the generated reasoning paths are not always correct. Therefore, they suggest that future research on improving the accuracy and reliability of the reasoning paths generated by models is important. The authors also raise unresolved questions, such as whether further scaling of model size will lead to further improvements in reasoning capabilities, and whether other prompting techniques might expand the scope of model applications.

Below are my thoughts on this paper.
This paper is a highly valuable piece of research that explains the Chain-of-Thought (CoT) method in detail and proves its effectiveness through numerous experimental results. Currently, commercially available LLMs (such as GPT-4o) have approximately 200B parameters and possess a broad knowledge base and general-purpose capabilities, but effective guidance methods for complex problems remain an important challenge. Traditional fine-tuning methods can improve performance in specific domains, but they have the problem of requiring significant computational resources and time. In comparison, CoT is an extremely useful technique because it efficiently draws out the model’s general reasoning capabilities with a small number of examples, significantly reducing costs. For example, in Educational Technology research, it is possible to refer to CoT to effectively improve LLM performance with fewer resources, which is far superior to traditional specialized fine-tuning methods. Also, the simplicity of CoT makes it easier for researchers to conduct various experiments, saving significant time and expenses. Furthermore, by referring to the experimental design of the paper, we can learn how to improve LLM performance in specific Educational Technology research contexts. For example, even in English composition correction experiments, by imitating the CoT format and providing appropriate examples, we can obtain more accurate correction suggestions from the model and collect high-quality research data. Additionally, due to the intermediate step output characteristic of CoT, the transparency and explainability of the solution process are enhanced, which I believe can be utilized more effectively in Educational Technology and actual instructional settings.