newsReddit r/MachineLearningTrust 72 · CommunityPublished 4d agoLive · 4d ago
RAGless: Q-Q retrieval with score aggregation for closed-domain FAQ [P]
What it does RAGless is a semantic retrieval system based on Question-to-Question matching. At ingestion, an LLM generates multiple question variants per answer (3–5) and each variant gets its own embedding. At query time, the user question is embedded, Top-K nearest question variants are retrieved, and scores are aggregated by answer_id — the answer with the highest aggregated score wins. Threshold logic uses two gates: minimum ag
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paperCovering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented GenerationpaperAB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question AnsweringpaperQuery-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge GraphspaperKnow Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented GenerationpaperLittle Brains, Big Feats: Exploring Compact Language ModelspaperEfficient Retrieval-Augmented Generation via Token Co-occurrence GraphspaperClinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering
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paperLittle Brains, Big Feats: Exploring Compact Language ModelspaperClinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question AnsweringpaperAsk, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-ImprovementpaperAB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question AnsweringpaperEfficient Retrieval-Augmented Generation via Token Co-occurrence GraphspaperQuery-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge GraphspaperKnow Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented GenerationpaperCovering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation