ReviewPatterns (New York, N.Y.)2025
A survey of multilingual large language models.
Review in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Multilingual Disparities in Large Language Model-Based Symptom Detection for Global Disease Surveillance: Evaluation Study.JMIR AI · 2026Article
- Article
- Performance of large language models in the Chinese National Medical Licensing Examinations and Beyond: Scoping review.PLOS digital health · 2026Article
- Performance of Large Language Models in Oral Health Consultations and the Consistency of the 'AI-as-a-Judge' Framework.International dental journal · 2026Article
- Autism-Related Information on Websites and General-Purpose Artificial Intelligence Chatbots: Comparative, Bilingual Study.JMIR formative research · 2026Article
- BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.Nature biomedical engineering · 2026Article
- Integrating speech biomarkers and large language models for adolescent suicide risk detection with mobile application for real-world evaluation.Cell reports. Medicine · 2026Article
- Benchmarking GPT-5, Gemini 2.5 Pro, Grok 4, and other LLMs on pediatric dentistry questions from a dental specialization exam.Scientific reports · 2026Article
- Culturally-Aware Prompting in conversational AI: supporting perceived communicative effectiveness in cross-cultural teams in Australia.Scientific reports · 2026Article
- FAIR-Birth: Development and Feasibility Testing of an AI-Supported Advance Birth Planning Application for Midwifery-Led Antenatal Care-A Mixed-Methods Study Protocol.Healthcare (Basel, Switzerland) · 2026Article
- The case for an integrated biobanking initiative in South Asia.The Lancet regional health. Southeast Asia · 2026Review
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- CuAgent provides a RAG-assisted intelligent framework to investigate cuproptosis.Scientific reports · 2026Article
- Korean Large Language Models for Medical Question Answering on Arthritis: Fine-tuning and Comparative Evaluation.Healthcare informatics research · 2026Article
- Quantifying uncertainty in protein representations across models and tasks.Nature methods · 2026Article
- Comparative performance of large language models in answering cornea and cataract surgery questions for resident training.BMC ophthalmology · 2026Article
- Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods Study.JMIR medical education · 2026Article
- Article
- How well do large language models mirror human cognition of word concepts?: A comparison of psychological ratings for early-acquired English words.Behavior research methods · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multilingual large language models (MLLMs) leverage advanced large language models to process and respond to queries across multiple languages, achieving significant success in polyglot tasks. Despite these breakthroughs, a comprehensive survey summarizing existing approaches and recent developments remains absent. To this end, this paper presents a unified and thorough review of the field, highlighting recent progress and emerging trends in MLLM research. The contributions of this paper are as follows. (1) Extensive survey: to our knowledge, this is the pioneering thorough review of multilingual alignment in MLLMs. (2) Unified taxonomy: we provide a unified framework to summarize the current progress in MLLMs. (3) Emerging frontiers: key emerging frontiers are identified, alongside a discussion of associated challenges. (4) Abundant resources: we collect abundant open-source resources, including relevant papers, data corpora, and leaderboards. We hope our work can provide the community quick access and spur breakthrough research in MLLMs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.