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New benchmark reveals LLMs struggle with Polish medical exams

By PulseAugur Editorial · [2 sources] ·

A new benchmark based on Polish medical exams has been developed to better assess the true competence of large language models (LLMs) in medicine. The benchmark, which includes over 15,000 questions and structural modifications to reduce biases, reveals that standard multiple-choice question answering formats can overestimate LLM capabilities. Even top-performing models like Qwen3.5-122B showed significant performance drops on this more rigorous evaluation. AI

IMPACT Highlights the need for more robust evaluation methods for medical LLMs, suggesting current benchmarks may not accurately reflect clinical readiness.

RANK_REASON The cluster contains an academic paper detailing a new benchmark for evaluating LLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark reveals LLMs struggle with Polish medical exams

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The cluster contains an academic paper detailing a new benchmark for evaluating LLMs.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Antoni Lasik, Jakub Pokrywka, {\L}ukasz Grzybowski, Jeremi Ignacy Kaczmarek, Gabriela Korza\'nska, Janusz \'Swieczkowski-Feiz, Oskar Pastuszek, Paulina Hoffman, Jakub Tomasz D\k{a}browski, Wojciech Kusa ·

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

    arXiv:2606.12250v1 Announce Type: new Abstract: Large language models (LLMs) in medicine are mainly evaluated using multiple-choice question answering (MCQA), which can overestimate real clinical ability due to guessing strategies and answer biases. To address these limitations, …

  2. arXiv cs.CL TIER_1 English(EN) · Wojciech Kusa ·

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

    Large language models (LLMs) in medicine are mainly evaluated using multiple-choice question answering (MCQA), which can overestimate real clinical ability due to guessing strategies and answer biases. To address these limitations, we introduce an expanded and more challenging be…

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