Chain of Thought (CoT) is a prompting technique that improves the reasoning capabilities of Large Language Models (LLMs) by generating intermediate reasoning steps[1]. It helps the LLM generate more accurate answers[1]. You can combine it with few-shot prompting to get better results on more complex tasks that require reasoning before responding, as it’s a challenge with a zero-shot chain of thought[1].
CoT's advantages[1]:
* It is low-effort while being very effective and works well with off-the-shelf LLMs (so no need to finetune).
* You also get interpretability with CoT prompting, as you can learn from the LLM’s responses and see the reasoning steps that were followed.
* Chain of thought appears to improve robustness when moving between different LLM versions, which means the performance of your prompt should drift less between different LLMs than if your prompt does not use reasoning chains.
The LLM response includes the chain of thought reasoning, which means more output tokens, which means predictions cost more money and take longer[1].
Self-consistency combines sampling and majority voting to generate diverse reasoning paths and select the most consistent answer[1]. It improves the accuracy and coherence of responses generated by LLMs[1].
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