LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
ScispaCy: Fast and robust models for biomedical natural language processing
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DPR-BAG generates factually grounded biomedical abstracts from full texts via structured BOMRC decomposition, parallel LLM prompting, and coherence refinement without any model training.
An LLM entity-tagging pipeline plus multi-agent system extracts ~6.3M nuanced records from 22.5M PubMed papers across six tasks with lower measured error than existing curated databases.
citing papers explorer
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LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking
LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
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Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation
DPR-BAG generates factually grounded biomedical abstracts from full texts via structured BOMRC decomposition, parallel LLM prompting, and coherence refinement without any model training.
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Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale
An LLM entity-tagging pipeline plus multi-agent system extracts ~6.3M nuanced records from 22.5M PubMed papers across six tasks with lower measured error than existing curated databases.