Topic 11: What are Chains (!) of Knowledge

Introduction

Since the introduction of Chain-of-Thought (CoT) prompting by Google Brain at NeurIPS 2022, the concept has sparked a wave of innovative methods and research, leading to a proliferation of "chain" spin-offs like Zero-Shot Chain-of-Thought and Multimodal Chain-of-Thought. We've covered the evolution of these ideas in Token 1.5: From Chain-of-Thoughts to Skeleton-of-Thoughts, and everything in between (it’s free to read), keeping a close watch on how CoT has inspired new lines of inquiry.

Today, we want to dive into the latest development: Chain-of-Knowledge (CoK). Since there are three (!) recent papers that claim to introduce the concept, we aim to clear up misunderstandings that have arisen due to researchers being unaware of each other's work. Our goal is also to map the influence of CoT and its successors, providing a clear understanding of how these approaches are reshaping the field of AI prompting and reasoning.

In today’s episode, we will cover:

• Recap of Chain-of-Thought Fundamentals

• Limitations of CoT Reasoning

• Here comes Chain-of-Knowledge. Or three Chains-of-Knowledge…

• Key contributions of each paper

• Experiments and results

• Can they be combined?

• Scenario: Complex, Multi-Domain Q-A Systems

• Conclusion: A Path to Stronger AI Reasoning

Tags: AI 101, Method/Techniques, Chain-of-Knowledge

Recap of Chain-of-Thought Fundamentals

The question of whether AI can truly reason like humans is a big topic in the field, with some thinking it might be a step toward artificial general intelligence (AGI). One method explored to enhance AI's reasoning is Chain-of-Thought (CoT) prompting. Unlike zero-shot prompting, which doesn’t provide any examples, or few-shot prompting, which includes a few examples, CoT prompting adds detailed reasoning steps to the examples. This makes it particularly useful for tasks that require more complex and logical thinking.

CoT prompting was developed to overcome the limitations of simpler prompting methods. It works by providing not just examples of problems and their solutions but also breaking down the reasoning process into a series of steps. This helps the model follow a logical sequence of thought, improving its ability to handle tasks that require more in-depth problem-solving. But it has its limitations as well →

Limitations of CoT Reasoning