Unlocking the Potential of AI through Chain of Thought Prompting
Artificial intelligence has advanced tremendously in recent years, with language models like GPT-3 demonstrating impressive natural language abilities. However, despite their progress, these large language models still struggle with complex, multi-step reasoning tasks. Problems like math word problems or commonsense reasoning remain challenging for AI.
To address this limitation, researchers have developed a novel technique called chain of thought prompting. Source: Wei et al (2022).
This method provides a way to enhance the reasoning capabilities of large language models like GPT-3.
How Chain of Thought Prompting Works
Chain of thought prompting guides the language model through a series of logical, intermediate steps when solving a complex problem.
Source: Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain of Thought Prompting Elicits Reasoning in Large Language Models.
Here’s another example of a math word problem:
"John had 35 marbles. He gave 8 marbles to Anna and 14 marbles to Tom. How many marbles does John have left?"
With standard prompting, you would provide the model with some input-output examples, and then ask it to solve the problem directly.
Chain of thought prompting works differently. Instead of jumping straight to the solution, it leads the model through reasoning steps:
- John originally had 35 marbles
- He gave 8 marbles to Anna
- So he now has 35 - 8 = 27 marbles
- He gave 14 marbles to Tom
- So he now has 27 - 14 = 13 marbles left
By structuring the prompt to demonstrate this logical progression, chain of thought prompting mimics the way humans break down problems step-by-step. The model learns to follow a similar reasoning process.
Why It Improves Reasoning
There are several key benefits to the chain of thought approach:
- It divides complex problems into smaller, more manageable parts. This allows the model to focus its vast computational resources on each sub-task.
- The intermediate steps provide interpretability into the model's reasoning process. This transparency makes it easier to evaluate the model's logic.
- Chain of thought prompting is versatile. It can enhance reasoning across diverse tasks like math, common sense, and symbol manipulation.
- The step-by-step structure improves learning efficiency. Models can grasp concepts more effectively when presented in a logical progression.
Research shows chain of thought prompting boosts performance on tasks requiring complex reasoning.
When It Works Best
However, chain of thought prompting only yields significant gains when used with extremely large models, typically those with over 100 billion parameters. The approach relies on the model having enough knowledge and processing power to successfully follow the reasoning steps.
Smaller models often fail to generate logical chains of thought, so chain of thought prompting does not improve their performance. The benefits appear to scale proportionally with model size.
In addition, the technique is best suited to problems with clear intermediate steps and language-based solutions. Tasks like mathematical reasoning lend themselves well to step-by-step reasoning prompts.
Unlocking Reasoning in AI
Chain of thought prompting offers an intriguing method to enhance reasoning in large AI models. Guiding the model to decompose problems into logical steps seems to unlock capabilities not accessible through standard prompting alone.
While not a universal solution, chain of thought prompting demonstrates how tailored prompting techniques can stretch the abilities of language models. As models continue to grow in scale, prompting methods like this will likely play an integral role in realizing the robust reasoning skills required for advanced AI.
- Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain of Thought Prompting Elicits Reasoning in Large Language Models.
- Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., … Fiedel, N. (2022). PaLM: Scaling Language Modeling with Pathways.
- Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., & Schulman, J. (2021). Training Verifiers to Solve Math Word Problems.