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The system generates multiple independent reasoning outputs for the same prompt, and then merges the results via consensus or voting for a final answer. Typical conditions for use: The stochastic diversity of models should be leveraged; The result must be robust against individual logic errors in single runs. The central trade-off: Significantly more robust…
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What organizes, connects, or makes decisions possible.
Self-Consistency generates multiple solution paths with a language model and selects their most frequent answer.
Wang and colleagues introduced Self-Consistency in 2022 as an extension of chain-of-thought reasoning. It arose from the observation that different reasoning paths for the same task can yield a more stable majority decision.
Ask several independent teams to solve the same calculation. If many reach the same result it seems more plausible, but a shared wrong assumption can still fool the majority.
One reasoning or answer path generated by the model.
The most frequent matching answer is selected.
Settings influence how different the generated paths are.
Self-Consistency can stabilize multi-step reasoning when independent paths converge. It costs extra computation and cannot detect a systematic error repeated across many paths.
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