Jul 2026· Health Information Science and Systems· Vol 14· 0 citations· 46 references
MedicineComputer Science
TL;DR
Experimental results show that BioRENLI consistently outperforms supervised fine-tuned LLM variants in both full-supervision and low-resource settings, while remaining competitive with strong BERT-based baselines, highlighting the effectiveness of enhancing biomedical RE with preference-aligned NLI.
Biomedical named entity recognition (NER) and relation extraction (RE) remain challenging because biomedical texts contain ambiguous abbreviations, complex entity boundaries, domain-specific terminology, and implicit relations. This study proposes a prompt-enhanced and QLoRA-adapted large language model framework for b...
Results demonstrate that InfoFlowEX equips LLMs with robust adaptability, achieving consistent gains over baselines with minimal task-specific customization, highlighting InfoFlowEX for real-world biomedical applications.
Wuyang Lan, Siqi Zhang, Wenzheng Wang et al.· Cell Reports Medicine· 0 citations
Scientific relation extraction aims to identify fine-grained semantic relations between domain-specific entities in scientific documents. However, complex scientific expressions, domain adaptation challenges, and subtle relation distinctions make this task difficult. To address these challenges, we propose a novel natu...
Yang Tian, Zi-Han Bai, Bo Xu et al.· IEEE Signal Processing Lette...· 0 citations
The rapid expansion of biomedical literature requires automated methods for accurate and efficient information extraction. This study addresses relation classification: given a pair of annotated biomedical entities in a research article title and abstract, assigning the relation that holds between them from a pre-defin...
Jannat, Charlie Dil, Tom Arodz et al.· Frontiers in Research Metric...· 0 citations
BELXTR is presented, a novel embedding model based on the multi-vector (a.k.a. late interaction) architecture, which allows to leverage token-level matching information in biomedical entity linking by integrating an existing task-specific training objective and exploring active query expansion.
Findings demonstrate that compact models can achieve strong biomedical classification performance through KD under compatible teacher–student pairings, while also highlighting that KD effectiveness varies substantially depending on the specific model combination.
