Multilingual QA

QA: Question Answering

Multilingual question answering studies whether models can retrieve, combine, and explain information when questions and evidence appear in different languages, including in spoken, visual, and specialized settings. Common approaches include multilingual retrieval and retrieval-augmented generation, translation and language routing, task-specific fine-tuning, and decomposition for multi-hop reasoning; benchmarks span factual, medical, legal, financial, geospatial, and speech tasks. Performance often varies across languages and cultural contexts: English evidence can help some systems but may also displace local sources, while low-resource languages often remain challenging. Open issues include building representative evaluations and reliable evidence checks, and balancing consistent answers across languages with respect for legitimate cultural variation.

Latest papers

63 papers9 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

Multilingual Medical Reasoning for Question Answering with Large Language Models
Dec 5, 2025Pietro Ferrazzi, Aitor Soroa, Rodrigo AgerriKnowledge Augmentation for Language Models Healthcare
Fondazione Bruno Kessler, Trento, Italy · HiTZ Center - Ixa, University of the Basque Country EHU, Spain
Zoom In Disparities in Healthcare LLM Q&A
Oct 20, 2025Ipek Baris Schlicht, Burcu Sayin, Zhixue Zhao +5Multilingual Language Model Evaluation Knowledge Augmentation for Language Models
Universitat Politècnica de València, Valencia, Spain · University of Trento, Trento, Italy · University of Sheffield, Sheffield, UK +5
Language Specific Knowledge: Do Models Know Better in X than in English?
May 21, 2025Ishika Agarwal, Nimet Beyza Bozdag, Dilek Hakkani-TürMultilingual Language Models Multilingual QA
Department of Computer Science University of Illinois, Urbana-Champaign

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Multilingual Medical Reasoning for Question Answering with Large Language Models

Pietro Ferrazzi, Aitor Soroa, Rodrigo Agerri

Fondazione Bruno Kessler, Trento, Italy · HiTZ Center - Ixa, University of the Basque Country EHU, Spain

Knowledge Augmentation for Language Models Healthcare Medical QA Multilingual QA Medical Reasoning Medical LLMs

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Large Language Models (LLMs) with reasoning capabilities have recently demonstrated strong potential in medical Question Answering (QA). Existing approaches are largely English-focused and primarily rely on distillation from general-purpose LLMs, raising concerns about the reliability of their medical knowledge. In this work, we present a method to generate multilingual reasoning traces based on medical knowledge extracted from Wikipedia. We produce 500k traces in English, Italian, and Spanish, using a retrieval-augmented generation approach over medical information from Wikipedia. The traces are generated to solve medical questions drawn from MedQA and MedMCQA, which we extend to Italian and Spanish. We test our pipeline in both in-domain and out-of-domain settings across Medical QA benchmarks, and demonstrate that our reasoning traces improve performance both when utilized via in-context learning (few-shot) and supervised fine-tuning, yielding state-of-the-art results among 8B-parameter LLMs. We believe that these resources can support the development of more transparent clinical decision-support tools in multilingual settings. We release the full suite of resources: reasoning traces, translated QA datasets, Medical-Wikipedia, and fine-tuned models.

Latest papers

Multilingual Medical Reasoning for Question Answering with Large Language Models

Zoom In Disparities in Healthcare LLM Q&A

Language Specific Knowledge: Do Models Know Better in X than in English?