Large language models like OpenAI's GPT-3 are supposed to provide better answers with what is called Chain of Thought (CoT) prompting
What is CoT Prompting and how can it help?
The article emphasizes the importance of prompt design in achieving optimal results and suggests that the use of AI in language models will become increasingly relevant in education.
What is CoT Prompting and how can it help?
Authors: Moritz Larsen, Prof. Dr. Doris Weßels
Summary
The article discusses the concept of "Chain of Thought" (CoT) prompting and its potential impact on improving the output of AI language models, specifically focusing on OpenAI's GPT-3 model. CoT prompting involves asking the model to explain its solution step by step, which can provide insights into the model's reasoning process. By using prompts that guide the model towards a step-by-step approach, the output can become more structured and transparent.
The article provides two examples to illustrate the effect of CoT prompting. In the first example, an arithmetic task is used, and the output with CoT prompting includes a step-by-step breakdown of the solution, whereas the output without CoT prompting only provides the final answer. In the second example, a quote explanation task is given, and the output with CoT prompting presents a more detailed and structured analysis of the quote, while the output without CoT prompting is a single block of text.
The article acknowledges that CoT prompting can improve the output of language models by making the results more understandable and informative. However, it also highlights the limitations of CoT prompting, as it only focuses on the output and does not provide insights into the inner workings of the underlying neural network. The article emphasizes the importance of prompt design in achieving optimal results and suggests that the use of AI in language models will become increasingly relevant in education.
Additionally, the article discusses the potential risks associated with anthropomorphizing AI and the need to maintain a critical distance when interacting with AI systems. It also mentions the exponential growth in the number of parameters in AI language models and the need for countries to develop their own AI ecosystems to reduce dependence on models trained with different cultural biases.
The authors of the article are Moritz Larsen, a master's student, and Prof. Dr. Doris Weßels, a professor of Information Systems, both affiliated with Kiel University of Applied Sciences in Germany.