RECIPER improves procedure-oriented retrieval from materials papers by combining paragraph-level dense retrieval with LLM-extracted procedural summaries and lightweight reranking, yielding average gains of +3.73 Recall@1 and better downstream QA.
Rag-fusion: a new take on retrieval-augmented generation
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CRVA-TGRAG combines parent-document segmentation, ensemble retrieval, and teacher-guided fine-tuning to mitigate knowledge conflicts and improve accuracy in LLM-based CVE vulnerability analysis.
Three-aspect RAG query pipeline optimization for cancer patient QA introduces HSRDR and SEOS and reports 5.24% accuracy gain on Claude-3-haiku versus chain-of-thought on a custom dataset.
citing papers explorer
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RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering
RECIPER improves procedure-oriented retrieval from materials papers by combining paragraph-level dense retrieval with LLM-extracted procedural summaries and lightweight reranking, yielding average gains of +3.73 Recall@1 and better downstream QA.
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Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations
CRVA-TGRAG combines parent-document segmentation, ensemble retrieval, and teacher-guided fine-tuning to mitigate knowledge conflicts and improve accuracy in LLM-based CVE vulnerability analysis.
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Query pipeline optimization for cancer patient question answering systems
Three-aspect RAG query pipeline optimization for cancer patient QA introduces HSRDR and SEOS and reports 5.24% accuracy gain on Claude-3-haiku versus chain-of-thought on a custom dataset.