Frontiers in Oncology (Jul 2023)

Evaluating large language models on a highly-specialized topic, radiation oncology physics

  • Jason Holmes,
  • Zhengliang Liu,
  • Lian Zhang,
  • Yuzhen Ding,
  • Terence T. Sio,
  • Lisa A. McGee,
  • Jonathan B. Ashman,
  • Xiang Li,
  • Tianming Liu,
  • Jiajian Shen,
  • Wei Liu

DOI
https://doi.org/10.3389/fonc.2023.1219326
Journal volume & issue
Vol. 13

Abstract

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PurposeWe present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs. This paper proposes evaluating LLMs on a highly-specialized topic, radiation oncology physics, which may be more pertinent to scientific and medical communities in addition to being a valuable benchmark of LLMs.MethodsWe developed an exam consisting of 100 radiation oncology physics questions based on our expertise. Four LLMs, ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and non-experts. The performance of ChatGPT (GPT-4) was further explored by being asked to explain first, then answer. The deductive reasoning capability of ChatGPT (GPT-4) was evaluated using a novel approach (substituting the correct answer with “None of the above choices is the correct answer.”). A majority vote analysis was used to approximate how well each group could score when working together.ResultsChatGPT GPT-4 outperformed all other LLMs and medical physicists, on average, with improved accuracy when prompted to explain before answering. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices across a number of trials, whether correct or incorrect, a characteristic that was not observed in the human test groups or Bard (LaMDA). In evaluating deductive reasoning ability, ChatGPT (GPT-4) demonstrated surprising accuracy, suggesting the potential presence of an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow for further improvement when scoring based on a majority vote across trials. In contrast, a team of medical physicists were able to greatly outperform ChatGPT (GPT-4) using a majority vote.ConclusionThis study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants.

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