A panel of smaller diverse LLMs outperforms a single large model as an evaluator of generations, showing less intra-model bias and over 7x lower cost.
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MSPA-CQR improves conversational query rewriting by constructing self-consistent preference data across rewriting, retrieval, and response dimensions and training with prefix-guided multi-faceted direct preference optimization, showing effectiveness in both in- and out-of-distribution settings.
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Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
A panel of smaller diverse LLMs outperforms a single large model as an evaluator of generations, showing less intra-model bias and over 7x lower cost.
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Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search
MSPA-CQR improves conversational query rewriting by constructing self-consistent preference data across rewriting, retrieval, and response dimensions and training with prefix-guided multi-faceted direct preference optimization, showing effectiveness in both in- and out-of-distribution settings.