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20242026
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cs.CL2026

Reassessing High-Performing LLMs on Polish Medical Exams: True Competence or Bias-Driven Performance?

Antoni Lasik, Jakub Pokrywka, Łukasz Grzybowski +7

Large language models (LLMs) in medicine are mainly evaluated using multiple-choice question answering (MCQA), which can overestimate real clinical ability due to guessing strategi…

cs.CL2025

Polish-English medical knowledge transfer: A new benchmark and results

Łukasz Grzybowski, Łukasz Grzybowski, Jakub Pokrywka +4

Large Language Models (LLMs) have demonstrated significant potential in handling specialized tasks, including medical problem-solving. However, most studies predominantly focus on…

cs.CL2025

Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning

Jeremi I. Kaczmarek, Jakub Pokrywka, Krzysztof Biedalak +3

Advances in Large Language Models revolutionized medical education by enabling scalable and efficient learning solutions. This paper presents a pipeline employing Retrieval-Augment…

cs.CL2024

Evaluating Transformer Models for Suicide Risk Detection on Social Media

Jakub Pokrywka, Jeremi I. Kaczmarek, Edward J. Gorzelańczyk +1

The detection of suicide risk in social media is a critical task with potential life-saving implications. This paper presents a study on leveraging state-of-the-art natural languag…

cs.CL2024

GPT-4 passes most of the 297 written Polish Board Certification Examinations

Jakub Pokrywka, Jeremi Kaczmarek, Edward Gorzelańczyk

Introduction: Recently, the effectiveness of Large Language Models (LLMs) has increased rapidly, allowing them to be used in a great number of applications. However, the risks pose…