PromptHub's summary of Chain of Thought techniques offers a great knowledge refresh and practical prompt templates.
𝐂𝐡𝐚𝐢𝐧 𝐨𝐟 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 (𝐂𝐨𝐓) prompting has emerged as a powerful technique to enhance the reasoning capabilities of large language models. This approach encompasses various methods, each with its unique strengths and applications. At its core, Zero-shot Chain of Thought is the simplest form, where adding a phrase like "Let's think about this step by step" can significantly improve a model's reasoning process. However, it's important to note that the optimal phrasing may vary between different LLMs.
𝐅𝐞𝐰-𝐬𝐡𝐨𝐭 𝐂o𝐓: This method involves providing examples that demonstrate the reasoning process, effectively enhancing the model's ability to tackle similar problems.
𝐒𝐞𝐥𝐟-𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐂𝐨𝐓: takes it a step further by generating multiple outputs using a CoT prompt and then using a self-consistency prompt to select the most consistent answer. This approach is particularly useful when exploring multiple reasoning paths.
𝐒𝐭𝐞𝐩-𝐛𝐚𝐜𝐤 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠: offers a practical variant of CoT, involving two main steps: abstraction, where a high-level view of the problem is requested, followed by solution generation based on this abstraction.
𝐀𝐧𝐚𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠: It encourages the model to generate relevant problems and their explanations before solving the problem at hand.
𝐓𝐡𝐫𝐞𝐚𝐝 𝐨𝐟 𝐓𝐡𝐨𝐮𝐠𝐡𝐭: For maintaining coherent reasoning across multiple turns, proves invaluable. This technique is ideal for longer Q&A sessions, retrieval-augmented generation, dialogues, and storytelling.
𝐂𝐨𝐧𝐭𝐫𝐚𝐬𝐭𝐢𝐯𝐞 𝐂𝐡𝐚𝐢𝐧 𝐨𝐟 𝐓𝐡𝐨𝐮𝐠𝐡𝐭: takes a unique approach by including both correct and incorrect examples, demonstrating faulty logic alongside correct reasoning to improve the model's discernment.
𝐅𝐚𝐢𝐭𝐡𝐟𝐮𝐥 𝐂𝐨𝐓: Addresses the issue of wrong answers despite seemingly correct reasoning steps. It combines natural language and symbolic reasoning. This two-step process involves translating a natural language query into a symbolic reasoning chain, followed by using a deterministic solver for the final answer.
𝐓𝐚𝐛𝐮𝐥𝐚𝐫 𝐂𝐨𝐓: Zero-shot CoT prompt used to instruct the model to provide reasoning in a structured format, often using markdown tables for clarity.
𝐀𝐮𝐭𝐨-𝐂𝐨𝐓: emphasizes diversity in questions and reasoning demonstrations. It involves two main steps: question clustering, where potential questions are divided into clusters, and demonstration sampling, which generates reasoning chains using zero-shot CoT for a question from each cluster.
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