LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
S cispa C y: F ast and R obust M odels for B iomedical N atural L anguage P rocessing
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DPR-BAG generates biomedical abstracts from full texts via BOMRC decomposition, parallel LLM summarization, and refinement, showing higher abstractive novelty than baselines while preserving factual consistency on a 46k-article PMC dataset.
This paper evaluates LLM voting ensembles for filtering noise in Wikidata-sourced mathematical concepts in Mathswitch, using MathWorld identifiers as positive control and grouping disagreements into three categories.
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 biomedical abstracts from full texts via BOMRC decomposition, parallel LLM summarization, and refinement, showing higher abstractive novelty than baselines while preserving factual consistency on a 46k-article PMC dataset.
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Categorizing Mathematical Concepts with LLM Voting Ensembles in Mathswitch
This paper evaluates LLM voting ensembles for filtering noise in Wikidata-sourced mathematical concepts in Mathswitch, using MathWorld identifiers as positive control and grouping disagreements into three categories.
- Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale