MiqraBERT, a finetuned Sentence-BERT model, achieves 2.7-fold better distributional separation of parallel versus non-parallel Biblical Hebrew verses and reduces ambiguous overlap from 24% to 6%, with strong performance on narrative but weak on poetic parallels.
Negative sampling for contrastive representation learning: A review
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MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
CARE, a context-aware LLM judge, outperforms standard methods when evaluating multi-hop retrieval quality in RAG systems.
A Multi-L KG and Quest-GNN with question-adaptive intra/inter-level message passing and synthesized pre-training data improves multi-hop RAG performance up to 33.8% on high-hop questions.
HYVINT introduces an intensity-driven incidence mechanism and tractable variational estimator for hypergraph generation, with error bounds and empirical gains in fidelity, novelty, and diversity.
citing papers explorer
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MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection
MiqraBERT, a finetuned Sentence-BERT model, achieves 2.7-fold better distributional separation of parallel versus non-parallel Biblical Hebrew verses and reduces ambiguous overlap from 24% to 6%, with strong performance on narrative but weak on poetic parallels.
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Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
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Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies
CARE, a context-aware LLM judge, outperforms standard methods when evaluating multi-hop retrieval quality in RAG systems.
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Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
A Multi-L KG and Quest-GNN with question-adaptive intra/inter-level message passing and synthesized pre-training data improves multi-hop RAG performance up to 33.8% on high-hop questions.
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HYVINT: Intensity-Driven Hypergraph Generation with Variational Representations
HYVINT introduces an intensity-driven incidence mechanism and tractable variational estimator for hypergraph generation, with error bounds and empirical gains in fidelity, novelty, and diversity.