Disco-RAG improves RAG by building intra-chunk discourse trees and inter-chunk rhetorical graphs that feed into a planning blueprint, delivering state-of-the-art results on question answering and long-document summarization without any fine-tuning.
arXiv preprint arXiv:2405.12035 (2024) 32
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NeocorRAG uses Evidence Chains to achieve SOTA retrieval quality in RAG on HotpotQA, 2WikiMultiHopQA, MuSiQue, and NQ for 3B and 70B models while using under 20% of the tokens of comparable methods.
EvoRAG adds a feedback-driven backpropagation step that attributes response quality to individual knowledge-graph triplets and updates the graph to raise reasoning accuracy by 7.34 percent over prior KG-RAG methods.
RDMA equips small LLMs with abbreviation resolution, phenotype reasoning, and ontology tools to mine rare diseases from EHR notes, outperforming fine-tuned and RAG baselines at up to 10x lower inference cost.
citing papers explorer
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Disco-RAG: Discourse-Aware Retrieval-Augmented Generation
Disco-RAG improves RAG by building intra-chunk discourse trees and inter-chunk rhetorical graphs that feed into a planning blueprint, delivering state-of-the-art results on question answering and long-document summarization without any fine-tuning.
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NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall via Evidence Chains
NeocorRAG uses Evidence Chains to achieve SOTA retrieval quality in RAG on HotpotQA, 2WikiMultiHopQA, MuSiQue, and NQ for 3B and 70B models while using under 20% of the tokens of comparable methods.
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EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation
EvoRAG adds a feedback-driven backpropagation step that attributes response quality to individual knowledge-graph triplets and updates the graph to raise reasoning accuracy by 7.34 percent over prior KG-RAG methods.
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RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records
RDMA equips small LLMs with abbreviation resolution, phenotype reasoning, and ontology tools to mine rare diseases from EHR notes, outperforming fine-tuned and RAG baselines at up to 10x lower inference cost.