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
5 Pith papers cite this work, alongside 20 external citations. Polarity classification is still indexing.
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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.
HYVINT generates hypergraphs by learning latent Poisson interaction intensities and diffusing hyperedge-side variational embeddings, with asymptotic generation-error bounds and improved structural fidelity in its reported experiments.
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.
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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HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings
HYVINT generates hypergraphs by learning latent Poisson interaction intensities and diffusing hyperedge-side variational embeddings, with asymptotic generation-error bounds and improved structural fidelity in its reported experiments.
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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.