The paper introduces Labadain-MedQA, a Tetun medical question‑answering dataset created by translating MedAlpaca’s Medical Flashcards and WikiDoc Patient Information into Tetun with Gemini 2.5 Flash, yielding 39,077 QA pairs. Translation quality was evaluated on 50 random pairs, showing human translations scored higher than LLM‑generated ones for medical accuracy and fluency (4.43/5 vs. 4.02/5, p < 0.001). Fine‑tuning Gemma and Llama models with parameter‑efficient methods on this dataset improved performance on Tetun‑translated USMLE multiple‑choice questions by up to 10.79% relative accuracy.
arXiv:2511.00421v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in medical applications, but their ability to detect and correct errors in clinical texts remains u...
arXiv:2608. 19981v1 Announce Type: new Abstract: We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.
arXiv:2610.34871v1 Announce Type: new Abstract: Objective: The rapid advancement of large language models (LLMs) in healthcare has opened new possibilities for AI-assisted clinical decision-making; h...
arXiv:2608. 12138v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings.
The paper introduces AyurParam-2.9B, a bilingual language model fine‑tuned for Ayurveda using a curated dataset of classical texts and clinical guidance in English and Hindi. It outperforms other open‑source instruction‑tuned models of similar size on the BhashaBench‑Ayur benchmark and rivals larger models in performance. The work demonstrates that authentic domain adaptation and high‑quality supervision are essential for reliable, culturally congruent AI in specialized medical fields.
arXiv:2609.18156v1 Announce Type: new Abstract: Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating l...
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present T...
arXiv:2601.02186v2 Announce Type: replace Abstract: Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large langua...
arXiv:2606. 24200v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) in clinical settings increasingly requires multilingual retrieval against predominantly English evidence corpora.
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
arXiv:2606. 07853v1 Announce Type: cross Abstract: Large Language Models are transforming the support for clinical decision and their application in real scenarios.
The paper presents a retrieval‑augmented generation (RAG) system combined with prompt engineering for Japanese‑to‑Chinese translation, focusing on sentences with noun‑modifying clause constructions (NMCCs). It integrates linguistic analysis, embedding‑based retrieval, and prompt construction before generating translations with GPT‑4o, without altering the base model. Experiments on a 66‑sentence test set show that increasing the knowledge‑base size from 0 to 2,000 examples raises macro‑averaged BLEU scores from 24.28 to 29.96, indicating improved translation quality for NMCC‑rich sentences.