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

PURPOSE

We 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.

METHODS

We 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.

RESULTS

ChatGPT 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.

CONCLUSION

This study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants.