arXiv:2607. 06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents.
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.
arXiv:2606. 24200v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) in clinical settings increasingly requires multilingual retrieval against predominantly English evidence corpora.
arXiv:2606. 15735v1 Announce Type: cross Abstract: Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmission, ongoing care, and diagnostic decision-making.
MMTClinic is a new benchmark that tests large language models on complex reasoning and question‑answering tasks involving clinical time‑series data. It combines text, medical images, and multivariate physiological signals to create 30,000 QA pairs—including 15,000 multiple‑choice and 15,000 open‑ended questions—in five languages (English, Hindi, Bengali, Marathi, and Tamil). The benchmark covers mortality prediction, heart‑rate forecasting, and SOFA score estimation, and evaluates 13 state‑of‑the‑art LLMs across zero‑shot, few‑shot, and chain‑of‑thought settings, revealing significant performance gaps across tasks, languages, and modalities.
The paper introduces a method for generating multilingual reasoning traces for medical question answering using large language models. It creates 500,000 reasoning traces in English, Italian, and Spanish by retrieving medical information from Wikipedia and applies them to MedQA and MedMCQA datasets extended into Italian and Spanish. The approach improves performance in both few‑shot in‑context learning and supervised fine‑tuning, achieving state‑of‑the‑art results for 8B‑parameter LLMs and releasing all resources for further research.
The BIT.UA team from the University of Aveiro participated in the 14th BioASQ Task B challenge, presenting a refactored modular pipeline for biomedical question answering. They replaced the PyTerrier PISA index with PostgreSQL-based pg_textsearch for BM25 retrieval and adopted Qdrant for dense embedding indexing, while also exploring HyDE-based query expansion and a Context-1 retrieval strategy. For answer generation, they introduced an LLM-as-a-judge framework and an agent quorum mechanism that allows multiple agents with diverse prompts to debate and converge on a consensus answer, achieving competitive MAP ranks of 5 in Phase A batches.
The paper introduces BRIE, a continuously maintainable benchmark for evaluating large language models (LLMs) in electronic health record (EHR) information retrieval. It presents a scalable framework that automatically generates question–answer pairs from longitudinal EHR notes, validated by nineteen clinicians. The benchmark allows assessment of multiple inference strategies and highlights that state‑of‑the‑art LLMs often miss clinically important information, especially when synthesis across documents is required.
The paper introduces BRIE, a scalable framework that automatically creates question–answer pairs from longitudinal electronic health record notes, validated by nineteen clinicians. It offers a continuously maintainable benchmark for evaluating large language models in clinical settings, addressing limitations of manual, costly, and quickly outdated existing benchmarks. Experiments across nine LLMs and five inference strategies reveal that even state‑of‑the‑art systems often miss clinically important information, especially for synthesis‑heavy queries.
This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.
arXiv:2607. 06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hallucinations and the rapid evolution of official guidance.
The PSK submission to the WMT 2026 Multilingual Instruction Shared Task employs a 3.35B‑parameter Tiny Aya Global model enhanced with three QLoRA adapters, each dedicated to a specific task: multilingual summarization, passage‑based question answering, and filtered standalone question answering. The summarization adapter is trained on multilingual document‑summary pairs, including scientific papers with author‑written abstracts, and outperforms a multitask adapter trained solely on organizer data on a held‑out split. For open question answering, results vary with answer length and evaluation method, prompting the submission of three systems that share the same context and summarization adapters but differ in their open‑QA adapters.