The paper introduces Distilled Rapid Embedding Transfer (DRET), a parameter‑efficient method that injects biomedical domain knowledge from large specialized models into a smaller general‑purpose model without retraining on the original specialized corpora. DRET evolves through iterative strategies—tokenizer‑merge (DRET 1.x), hybrid embedding averaging (DRET 2.0), priority‑based embedding transfer (DRET 3.x), and further refinements (DRET 4.x)—and demonstrates that a 66‑million‑parameter DistilBERT can achieve competitive or superior performance on token‑level PICO classification compared to much larger models, while remaining lightweight. The authors validate the embedding‑level transfer with cosine similarity, semantic‑shift, and t‑SNE analyses, highlighting DRET’s potential for scalable, resource‑efficient biomedical text mining.
arXiv:2609.22734v1 Announce Type: cross Abstract: Clinical domain classification plays an important role in organizing and analyzing large volumes of unstructured medical text. However, medical trans...
This thesis explores how to select and adapt NLP models for global health literature when annotated data and computational resources are scarce. It compares skip‑gram word2vec models trained on increasingly large specialized corpora with BioWordVec for semantic tag discovery, finding that larger coverage does not always yield more useful domain associations. The study also evaluates convolutional spaCy models versus a RoBERTa transformer for named entity recognition, noting a trade‑off between higher F1 scores and longer inference time, and investigates MiniLM few‑shot versus BART‑MNLI zero‑shot classification for multi‑label topic classification, highlighting practical constraints of inference cost. "whyItMatters":"The work provides empirical guidance on balancing model accuracy and resource demands for building knowledge systems in low‑resource global health settings."
arXiv:2606. 18703v1 Announce Type: new Abstract: Pretrained biological language models expose per-token probability distributions through masked-token prediction, providing the likelihood interface central to sequence design, variant scoring, and mechanistic interpretation.
arXiv:2607. 20465v1 Announce Type: new Abstract: The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end.
BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.
The paper introduces a web‑data curation recipe for pretraining medical encoders, addressing the scarcity of large, diverse corpora in dense‑terminology domains like medicine. It proposes two complementary techniques: medical‑term density filtering to select documents rich in medical terminology, and signal‑amplifying rephrasing that uses an LLM to rewrite documents into denser variants with broader entity contexts. Applied to French medical NLP, the recipe produces the FineMed corpus and the DoctoBERT encoder family, achieving state‑of‑the‑art results on the DrBenchmark public benchmark and a proprietary clinical NER task.
arXiv:2606. 28353v1 Announce Type: cross Abstract: Linking FDA-approved medical devices to their underlying United States Patent and Trademark Office (USPTO) patents enables critical applications such as recall root-cause analysis, M&A-driven IP discovery, and technology trajectory mapping.
The paper introduces a unified framework for aligning biomedical text with knowledge graphs using a lightweight projection learned via contrastive learning, keeping the text encoder and KG embedding model frozen. It evaluates six design choices—text encoder, KG embedding, projection head, triple composition, training direction, and hard‑negative sampling—on a newly created CTD‑Align corpus of 22K chemical‑gene interaction pairs linked to PubMed passages. The study finds that triple composition and training direction have the largest impact, while simpler linear projections over concatenated subject, predicate, and object embeddings yield the best performance.
arXiv:2606. 28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables.
The paper benchmarks six long‑tail loss functions—cross‑entropy, weighted CE, class‑balanced loss, focal loss, LDAM, and logit‑adjusted softmax—across three single‑cell foundation model architectures (scGPT, scBERT, Geneformer) and three datasets (Multiple Sclerosis, Zheng68K, human Pancreas). It shows that overall accuracy masks systematic failures on rare, disease‑relevant cell types, with a consistent gap between overall accuracy, Macro‑F1, and rare‑class recall under plain cross‑entropy. The study identifies two distinct regimes of rare‑class failure, predicts reweighting efficacy by absolute training‑set size, and finds class‑balanced loss and LDAM to be the most reliable across all settings.
arXiv:2607. 20163v1 Announce Type: cross Abstract: The rapid growth of biomedical knowledge has made the validation of automatically generated biological annotations a major bottleneck in biomedical curation.
arXiv:2607. 08490v1 Announce Type: new Abstract: Biomedical language evolves rapidly as new discoveries emerge, causing traditional text models to lose semantic fidelity over time.