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Future Internet
Volume 17
Issue 6
10.3390/fi17060235
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

LLM Performance in Low-Resource Languages: Selecting an Optimal Model for Migrant Integration Support in Greek

by
Alexandros Tassios
1,
Stergios Tegos
1,
Christos Bouas
1,
Konstantinos Manousaridis
1,
Maria Papoutsoglou
1,
Maria Kaltsa
2,3,
Eleni Dimopoulou
4,
Thanassis Mavropoulos
3,
Stefanos Vrochidis
3 and
Georgios Meditskos
1,*
1
School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Department of Theoretical & Applied Linguistics, School of English, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Information Technologies Institute, Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
4
PRAKSIS, 10432 Athens, Greece
*
Author to whom correspondence should be addressed.
Future Internet 2025, 17(6), 235; https://doi.org/10.3390/fi17060235
Submission received: 21 April 2025 / Revised: 21 May 2025 / Accepted: 22 May 2025 / Published: 26 May 2025
Versions Notes

Abstract

The integration of Large Language Models (LLMs) in chatbot applications gains momentum. However, to successfully deploy such systems, the underlying capabilities of LLMs must be carefully considered, especially when dealing with low-resource languages and specialized fields. This paper presents the results of a comprehensive evaluation of several LLMs conducted in the context of a chatbot agent designed to assist migrants in their integration process. Our aim is to identify the optimal LLM that can effectively process and generate text in Greek and provide accurate information, addressing the specific needs of migrant populations. The design of the evaluation methodology leverages input from experts on social assistance initiatives, social impact and technological solutions, as well as from automated LLM self-evaluations. Given the linguistic challenges specific to the Greek language and the application domain, research findings indicate that Claude 3.7 Sonnet and Gemini 2.0 Flash demonstrate superior performance across all criteria, with Claude 3.7 Sonnet emerging as the leading candidate for the chatbot. Moreover, the results suggest that automated custom evaluations of LLMs can align with human assessments, offering a viable option for preliminary low-cost analysis to assist stakeholders in selecting the optimal LLM based on user and application domain requirements.
Keywords: chatbot; Greek language; large language models; LLM evaluation; migrant integration chatbot; Greek language; large language models; LLM evaluation; migrant integration

Share and Cite

MDPI and ACS Style

Tassios, A.; Tegos, S.; Bouas, C.; Manousaridis, K.; Papoutsoglou, M.; Kaltsa, M.; Dimopoulou, E.; Mavropoulos, T.; Vrochidis, S.; Meditskos, G. LLM Performance in Low-Resource Languages: Selecting an Optimal Model for Migrant Integration Support in Greek. Future Internet 2025, 17, 235. https://doi.org/10.3390/fi17060235

AMA Style

Tassios A, Tegos S, Bouas C, Manousaridis K, Papoutsoglou M, Kaltsa M, Dimopoulou E, Mavropoulos T, Vrochidis S, Meditskos G. LLM Performance in Low-Resource Languages: Selecting an Optimal Model for Migrant Integration Support in Greek. Future Internet. 2025; 17(6):235. https://doi.org/10.3390/fi17060235

Chicago/Turabian Style

Tassios, Alexandros, Stergios Tegos, Christos Bouas, Konstantinos Manousaridis, Maria Papoutsoglou, Maria Kaltsa, Eleni Dimopoulou, Thanassis Mavropoulos, Stefanos Vrochidis, and Georgios Meditskos. 2025. "LLM Performance in Low-Resource Languages: Selecting an Optimal Model for Migrant Integration Support in Greek" Future Internet 17, no. 6: 235. https://doi.org/10.3390/fi17060235

APA Style

Tassios, A., Tegos, S., Bouas, C., Manousaridis, K., Papoutsoglou, M., Kaltsa, M., Dimopoulou, E., Mavropoulos, T., Vrochidis, S., & Meditskos, G. (2025). LLM Performance in Low-Resource Languages: Selecting an Optimal Model for Migrant Integration Support in Greek. Future Internet, 17(6), 235. https://doi.org/10.3390/fi17060235

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