Self-Consistency

Self-consistency prompting is a technique that's aimed at enhancing the quality of outputs from AI models by leveraging the model's ability to check its own answers. Originally proposed by Wang et al. (2022)https://arxiv.org/abs/2203.11171, self-consistency aims "to replace the naive greedy decoding used in chain-of-thought prompting". Think of it like a student cross-referencing their own exam answers to ensure they haven't contradicted themselves. By prompting the AI model in a manner that encourages it to review and validate its own outputs, this technique aims to boost consistency and reduce errors in the model's responses.

The concept of "naive greedy decoding" in chain-of-thought prompting refers to the model's tendency to follow the most probable path of generating text based on its training, without the ability to reconsider or reflect on its choices. This approach, while efficient, can lead to errors or inconsistencies, as the model may not adequately consider alternative interpretations or solutions.

In contrast, self-consistency prompting works by encouraging the AI to generate multiple answers or explanations for a given query, and then cross-examine these to determine the most consistent and logical response. This method is like having the model perform an internal peer review, where it challenges and verifies its initial conclusions.

By doing so, self-consistency reduces the likelihood of errors that can occur due to the linear and one-directional nature of greedy decoding. The AI model is no longer just following the path of highest immediate probability. Instead, it takes a step back to evaluate the broader context and coherence of its responses. This shift from a single, immediate ...