When Multilingual Models Compete with Monolingual Domain-Specific Models in Clinical Question Answering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511613" target="_blank" >RIV/00216208:11320/25:10511613 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2025.cl4health-1.6/" target="_blank" >https://aclanthology.org/2025.cl4health-1.6/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2025.cl4health-1.6" target="_blank" >10.18653/v1/2025.cl4health-1.6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

When Multilingual Models Compete with Monolingual Domain-Specific Models in Clinical Question Answering

Popis výsledku v původním jazyce

This paper explores the performance of multilingual models in the general domain on the clinical Question Answering (QA) task to observe their potential medical support for languages that do not benefit from the existence of clinically trained models. In order to improve the model’s performance, we exploit multilingual data augmentation by translating an English clinical QA dataset into six other languages. We propose a translation pipeline including projection of the evidences (answers) into the target languages and thoroughly evaluate several multilingual models fine-tuned on the augmented data, both in mono- and multilingual settings. We find that the translation itself and the subsequent QA experiments present a differently challenging problem for each of the languages. Finally, we compare the performance of multilingual models with pretrained medical domain-specific English models on the original clinical English test set. Contrary to expectations, we find that monolingual domain-specific pretrai

Název v anglickém jazyce

When Multilingual Models Compete with Monolingual Domain-Specific Models in Clinical Question Answering

Popis výsledku anglicky

This paper explores the performance of multilingual models in the general domain on the clinical Question Answering (QA) task to observe their potential medical support for languages that do not benefit from the existence of clinically trained models. In order to improve the model’s performance, we exploit multilingual data augmentation by translating an English clinical QA dataset into six other languages. We propose a translation pipeline including projection of the evidences (answers) into the target languages and thoroughly evaluate several multilingual models fine-tuned on the augmented data, both in mono- and multilingual settings. We find that the translation itself and the subsequent QA experiments present a differently challenging problem for each of the languages. Finally, we compare the performance of multilingual models with pretrained medical domain-specific English models on the original clinical English test set. Contrary to expectations, we find that monolingual domain-specific pretrai

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)

ISBN

979-8-89176-238-1

ISSN

—

e-ISSN

—

Počet stran výsledku

14

Strana od-do

69-82

Název nakladatele

Association for Computational Linguistics

Místo vydání

Kerrville, TX, USA

Místo konání akce

Albuquerque, NM, USA

Datum konání akce

4. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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