Edge 369: LLM Reasoning with Chain-Of-Code

Can LLMs use code generation to reason through complex tasks?

Feb 13, 2024

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Visualize a less robotic and more abstract representation of an artificial intelligence model, now depicted as an ethereal, digital entity hovering over a computer. The entity is composed of swirling digital patterns and light, symbolizing its advanced cognitive abilities. Its "presence" is focused on the screen, which still displays the flowchart breaking down a complex task into smaller, manageable steps. Surrounding the entity, holographic projections of code snippets and algorithm patterns float in the airy space, enhancing the futuristic ambiance. The setting remains a modern, dimly lit room with ambient blue and green lighting, emphasizing the AI's innovative approach to programming and problem-solving. This image captures the transition from a physical robot to a more formless, digital intelligence, engaged in the same task of generating programming code. — Created Using DALL-E

In this Issue:

Understanding chain-of-code(CoC) for LLM Reasoning.
A review of the Google DeepMind’s original CoC paper.
An introduction to the popular Embedchain RAG framework.

💡 ML Concept of the Day: What is Chain-of-Code?

Writing code is a clear expression of reasoning. A typical program includes blocks that express control-flow, logical expressions and modular structures that combine to model a solution to a given problem. Could code be used to improve LLM reasoning. That was the thesis behind the chain-of-code(CoC) method.

TheSequence

Edge 369: LLM Reasoning with Chain-Of-Code

Can LLMs use code generation to reason through complex tasks?

In this Issue:

💡 ML Concept of the Day: What is Chain-of-Code?

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