Exploring Large Language Models in Medical Question Answering

Khái niệm cốt lõi

The author explores the performance of various large language models in medical question answering, highlighting similarities and differences between models and their correlation with human performance.

Tóm tắt

The study benchmarks eight large language models on a dataset of medical questions from Polish licensing exams. Larger models generally outperformed smaller ones, with similarities observed across models. Model accuracy was influenced by question length and the probability assigned to the correct answer. The study provides insights into the performance patterns of large language models in medical applications.

Thống kê

LLM accuracies were positively correlated pairwise (0.29 to 0.62).
Model performance was also correlated with human performance (0.07 to 0.16).
The top scoring LLM, GPT-4 Turbo, scored 82%.
Med42, PaLM 2, Mixtral, and GPT-3.5 scored around 63%.

Trích dẫn

"We found evidence of similarities between models in which questions they answer correctly."
"Larger models typically performed better, but differences in training methods were also highly impactful."

Thông tin chi tiết chính được chắt lọc từ

Exploring the landscape of large language models in medical question answering

by Andrew M. Be... lúc arxiv.org 03-12-2024

https://arxiv.org/pdf/2310.07225.pdf

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