LLMs are impressive feats of pattern recognition, able to sift through mountains of data and find connections invisible to the human eye. Yet, even wi

Let’s think step by step: Chain of Thought prompting in LLMs

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2024-12-23 22:00:07

LLMs are impressive feats of pattern recognition, able to sift through mountains of data and find connections invisible to the human eye. Yet, even with this prodigious ability, they sometimes stumble when faced with complex reasoning. They might offer a correct answer, but it's more akin to a lucky guess than genuine understanding. Chain-of-Thought prompting (CoT) offers a compelling solution to this intriguing paradox.

By providing the LLM with a "chain" of reasoning—a series of logical stepping stones composed of intermediate steps, justifications, and supporting evidence—we guide it through the problem-solving process. It's like giving the LLM a cognitive map, enabling it to navigate the intricate pathways of logic and arrive at an answer not through chance, but through genuine comprehension.

A 2022 Google Research paper, "Chain of Thought Prompting Elicits Reasoning in Large Language Models,"(https://arxiv.org/pdf/2201.11903) introduced the concept of Chain-of-Thought (CoT) prompting. The researchers demonstrated that guiding large language models through intermediate reasoning steps, expressed in natural language, significantly boosted their performance on complex tasks.