arXiv AI

Analyzing and Mitigating Cross-Lingual Degradation in Multilingual Medical VQA

arXiv AI

Jun 9

Multilingual Training and Evaluation Resources for Vision-Language Models

arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.

By Daniela Baiamonte, Elena Fano, Matteo Gabburo, Stefano Simonazzi, Leonardo Rigutini, Andrea Zugarini

llms multimodal benchmarks

Hugging Face Trending Papers

Jun 24

Multilingual Hematology Visual Question Answering Dataset

Vision Language Models (VLMs) have shown promising capabilities in medical image analysis by jointly understanding visual and textual information for tasks such as Visual Question Answering. However, existing hematology vision-language resources remain predominantly English centric, limiting their applicability in multilingual healthcare environments.

llms nlp multimodal benchmarks

arXiv Computation and Language

2d ago

Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning

The paper introduces PM4Bench, a multimodal, multilingual, multi-task benchmark built on a strictly parallel 10‑language corpus, allowing fair cross‑lingual comparison of Large Vision‑Language Models (LVLMs). It also proposes a vision setting that embeds textual inputs directly into images to better mimic real deployment scenarios. Experiments show OCR performance drives cross‑lingual gaps, leading to an OCR‑centric GRPO training strategy that improves multilingual VQA and reduces disparities without costly supervision.

By Junyuan Gao, Jiahe Song, Jiang Wu, Runchuan Zhu, Guanlin Shen, Shasha Wang, Xingjian Wei, Haote Yang, Weijia Li, Bin Wang, Lijun Wu, Conghui He

llms agents computer-vision reinforcement-learning multimodal benchmarks

arXiv AI

Jun 6

Noise-Aware Visual Representation Learning for Medical Visual Question Answering

arXiv:2606. 05535v1 Announce Type: cross Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries.

By I Putu Adi Pratama, Bahadorreza Ofoghi, Atul Sajjanhar, Shang Gao

llms rag nlp fine-tuning benchmarks

arXiv Computation and Language

6d ago

HealMed: Multilingual Evaluation of Large Language Models in Medicine

arXiv:2608. 19981v1 Announce Type: new Abstract: We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.

By Yingjian Chen (Drew), Fan Gao (Drew), Sherry T. Tong (Drew), Haoyu Zhang (Drew), Aosong Feng (Drew), Kevin W. Jin (Drew), Xing Wu (Drew), Jinghui Lu (Drew), Abdul Samad (Drew), Akbar Faruqi (Drew), Cesar Caraballo (Drew), Cibele Brand\~ao (Drew), Dhruva (Drew), Gupta, Eunji Jeon, Gabriel Madera-Santiago, Geon Lee, Hugo Toshio Itikawa, Insook Cho, Isabelli Martins, Isarar Siddique, Israr Ahmed, Jihyo Kwak, Kanyakorn Veerakanjana, Luis Guilherme Cardoso, Minjin Kim, Piyalitt Ittichaiwong, Renee Dua, Santiago Gudi\~no-Rosales, Xiujie Chen, Zeo Lapalus, Zixin Xu, Michihiro Yasunaga, Rex Ying, Heuiseok Lim, Jaewoo Kang, Chanjun Park, Hang Jiang, Ethan Goh, Hyunjae Kim, Edison Marrese-Taylor, Yusuke Iwasawa, Yutaka Matsuo, Qingyu Chen, Irene Li

llms benchmarks

arXiv AI

Aug 14

Vision-Language Models are Fragile Multilingual Associators

arXiv:2608. 12333v1 Announce Type: cross Abstract: Vision-language models must associate visual entities with textual attributes.

By Ritabrata Chakraborty, Rajatsubhra Chakraborty, Shivakumara Palaiahnakote, Angelo Cangelosi, Umapada Pal

llms multimodal benchmarks

arXiv AI

Jul 15

An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering

arXiv:2607. 12048v1 Announce Type: cross Abstract: Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired knowledge.

By Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed, Dilnaz Utemissova, Ufaq Khan, Mohammad Yaqub

llms nlp multimodal

arXiv AI

Aug 14

Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights

arXiv:2506. 17337v5 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown promise in automating image diagnosis and interpretation in clinical settings.

By Yuan Zhong, Ruinan Jin, Qi Dou, Xiaoxiao Li

llms fine-tuning benchmarks

Hugging Face Trending Papers

Jul 30

LoMeVQA: A Comprehensive Benchmark for Longitudinal Medical VQA

In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal information is crucial for reliable assessment of disease progression and treatment response.

llms nlp multimodal benchmarks

arXiv AI

Jun 11

OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models

arXiv:2606. 12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers.

By Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci, Abeer Badawi, Adibvafa Fallahpour, Arash Afkanpour, Leonid Sigal, Ali Etemad, Elham Dolatabadi

llms fine-tuning multimodal benchmarks safety

arXiv AI

Aug 12

MedUP: Awakening Unified Understanding and Perception in Medical Vision-Language Models

arXiv:2608. 10635v1 Announce Type: cross Abstract: Medical Vision-Language Models (Med-VLMs) excel at verbalizing visual content, yet precise visual perception, segmentation, and grounding remain challenging.

By Yuan Wang, Hualiang Wang, Yixin Chen, Songtao Jiang, Shujian Gao, Jiaming Lin, Siming Fu, Jian Wu, Zuozhu Liu

llms agents computer-vision nlp multimodal safety

arXiv AI

Jul 21

MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering

arXiv:2604. 09757v2 Announce Type: replace-cross Abstract: Medical vision--language models (VLMs) have shown strong potential for medical visual question answering (VQA), yet their reasoning remains largely text-centric: images are encoded once as static context, and subsequent inference is dominated by language.

By Suyang Xi, Songtao Hu, Yuxiang Lai, Wangyun Dan, Yaqi Liu, Shansong Wang, Xiaofeng Yang

llms rag nlp fine-tuning benchmarks

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