NNN decoding selects documents via non-negative elastic net reconstruction of the query embedding, with a theorem showing it strictly dominates dense retrieval on correlated corpora and experiments showing gains over inner-product baselines.
Vendi-RAG: Adaptively trading-off diversity and quality significantly improves retrieval augmented generation with LLMs.arXiv preprint arXiv:2502.11228, 2025
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GuarantRAG improves RAG accuracy up to 12.1% and cuts hallucinations 16.3% by decoupling parametric reasoning from evidence integration via contrastive DPO and joint decoding.
CARRIAGE is a RAG framework that improves output diversity in cross-cultural recipe adaptation by enhancing retrieval and context handling, reaching Pareto efficiency on diversity and quality versus closed-book LLMs.
citing papers explorer
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Non-negative Elastic Net Decoding for Information Retrieval
NNN decoding selects documents via non-negative elastic net reconstruction of the query embedding, with a theorem showing it strictly dominates dense retrieval on correlated corpora and experiments showing gains over inner-product baselines.
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Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
GuarantRAG improves RAG accuracy up to 12.1% and cuts hallucinations 16.3% by decoupling parametric reasoning from evidence integration via contrastive DPO and joint decoding.
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Culinary Crossroads: A RAG Framework for Enhancing Diversity in Cross-Cultural Recipe Adaptation
CARRIAGE is a RAG framework that improves output diversity in cross-cultural recipe adaptation by enhancing retrieval and context handling, reaching Pareto efficiency on diversity and quality versus closed-book LLMs.