Giving your AI time to think
Humans make better decisions when they think them through, but we expect AIs to respond with the right answer right away.
I've been doing some research on chain of thought prompting and its potential benefits for our language models. It seems like a promising technique to improve accuracy. \n
However, we also need to consider the trade-off with speed. Implementing chain of thought may slow down our models, and we need to find the right balance. \n
But I believe this technique can truly elevate our performance. Let's give our AI the time to think and see the impact it makes. \n
By dividing tasks into system one and system two thinking, our models will become more deliberate in their approach. This will lead to better accuracy in the long run. \n
Let's dive into the tutorial and learn how to effectively implement chain of thought in our systems.
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In recent years, large language models (LLMs) have made significant progress in natural language processing tasks. However, one common challenge faced by LLMs is their inability to reason through complex problems. To address this limitation, researchers have introduced a powerful prompting technique called "chain of thought." In this blog post, we will explore the concept of chain of thought prompting and its potential to improve the performance of AI systems.
Understanding the Chain of Thought:
Chain of thought prompting aims to mimic the way humans think and reason. It divides the thinking process into two systems: system one and system two. System one represents the automatic, subconscious decision-making process, while system two involves conscious, deliberate thinking. LLMs primarily operate in system one, providing immediate responses without explicitly reasoning through the steps. However, by implementing chain of thought prompting, we can make LLMs stop and think through the problem, leading to more deliberate and accurate responses.
Implementing Chain of Thought Prompting:
Implementing chain of thought prompting is relatively straightforward. By providing the AI system with step-by-step reasoning examples, we can guide it to follow a more deliberate approach. For example, we can structure the prompt to include explicit instructions like "think step by step" or use visual cues such as dashes to indicate the steps. By demonstrating to the AI system how it should reason through the problem, we can improve its performance significantly.
Evaluating the Response:
To evaluate the effectiveness of chain of thought prompting, it is crucial to measure the accuracy of the AI system's responses. One way to evaluate the response is by comparing it to the correct answer. In the provided transcript, an evaluation function is used to extract the final answer from the AI system's response and check if it follows the specified format. Additionally, the evaluation function can also assess whether the response includes reasoning steps or not. This evaluation process allows us to assess the AI system's performance and debug any potential errors.
Comparing Standard Prompting and Chain of Thought Prompting:
To compare the performance of standard prompting and chain of thought prompting, the evaluation metrics can be used. In the transcript, the results show that standard prompting achieves 90% accuracy but lacks reasoning steps, making it challenging to debug errors. On the other hand, chain of thought prompting achieves 100% accuracy but takes slightly longer to process. This trade-off between accuracy and speed is an essential consideration when deciding whether
Chain of thought is one of the most common prompting techniques it's really useful for getting alarms. The reason they're still not great at it, but this makes a really big difference in terms of performance. And it's very easy to implement. We're going to walk through this. There is a paper that you can read on chain of thought, and it actually it's used in many scientific papers.
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