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Comparative reasoning plays a crucial role in text preference prediction;however, large language models (LLMs) often demonstrate inconsistencies intheir reasoning. While approaches like Chain-of-Thought improve accuracy inmany other settings, they struggle to consistently distinguish the similaritiesand differences of complex texts. We introduce SC, a prompting approach thatpredicts text preferences by generating structured intermediate comparisons. SCbegins by proposing aspects of comparison, followed by generating textualcomparisons under each aspect. We select consistent comparisons with a pairwiseconsistency comparator that ensures each aspect's comparisons clearlydistinguish differences between texts, significantly reducing hallucination andimproving consistency. Our comprehensive evaluations across various NLP tasks,including summarization, retrieval, and automatic rating, demonstrate that SCequips LLMs to achieve state-of-the-art performance in text preferenceprediction.

On What Basis? Predicting Text Preference Via Structured Comparative Reasoning