mlsanityData to 7 Oct 2026

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

arXiv 2405.01589 ↗First posted 29 Apr 2024cs.CL cs.AIcited by 10

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Abstract (arXiv)

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 posed by the generation of false information through LLMs significantly limit their applications in sensitive areas such as healthcare, highlighting the necessity for rigorous validations to determine their utility and reliability. To date, no study has extensively compared the performance of LLMs on Polish medical examinations across a broad spectrum of specialties on a very large dataset. Objectives: This study evaluated the performance of three Generative Pretrained Transformer (GPT) models on the Polish Board Certification Exam (Pa'nstwowy Egzamin Specjalizacyjny, PES) dataset, which consists of 297 tests. Methods: We developed a software program to download and process PES exams and tested the performance of GPT models using OpenAI Application Programming Interface. Results: Our findings reveal that GPT-3.5 did not pass any of the analyzed exams. In contrast, the GPT-4 models demonstrated the capability to pass the majority of the exams evaluated, with the most recent model, gpt-4-0125, successfully passing 222 (75%) of them. The performance of the GPT models varied significantly, displaying excellence in exams related to certain specialties while completely failing others. Conclusions: The significant progress and impressive performance of LLM models hold great promise for the increased application of AI in the field of medicine in Poland. For instance, this advancement could lead to the development of AI-based medical assistants for healthcare professionals, enhancing the efficiency and accuracy of medical services.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 13 of its 45 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

13 of 45 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
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  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  7. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
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  10. 2016Enriching Word Vectors with Subword Informationcited by 1,578
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  12. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
1 more
  1. 2023GPT-4 can pass the Korean National Licensing Examination for Korean Medicine Doctorscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 3.

10 papers.