Combining contrastive loss with KLD distillation and adding sparsity regularization improves effectiveness and reduces FLOPS by 2x in conversational search with minimal recall loss.
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Headache specialists preferred their own literature summaries over those from Sonnet, GPT-4o, and Llama 3.1 in a blinded evaluation, though AI summaries were sometimes indistinguishable.
A multi-turn RAG system combines learned sparse retrieval with LLM-conditioned rewriting, listwise reranking, and generation to handle conversational QA and unanswerable queries across four domains.
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Improving the Efficiency and Effectiveness of LLM Knowledge Distillation for Conversational Search
Combining contrastive loss with KLD distillation and adding sparsity regularization improves effectiveness and reduces FLOPS by 2x in conversational search with minimal recall loss.