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Flip-Flop Consistency: Unsupervised Training for Robustness to Prompt Perturbations in LLMs
Large Language Models (LLMs) often produce inconsistent answers when faced with different phrasings of the same prompt. In this paper, we propose Flip-Flop Consistency ($F^2C$), an unsupervised training method that improves robustness to such perturbations. $F^2C$ is composed of two key components.
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