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1,505 claims from 935 papers are on the record. 46 have been checked so far; the other 1,459 have no check with a result yet.

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Keyword: PubMedQA ×Clear all

7 claims from 3 papers

1. Biochemistry, Genetics and Molecular Biology › Biomedical Text Mining and Ontologies

   BioGPT: generative pre-trained transformer for biomedical text generation and mining

   Luo, Sun, Xia et al. · Briefings in Bioinformatics · 2022

   The authors present BioGPT, a generative language model pre-trained on biomedical literature, and report that it outperforms previous models on most of six biomedical language tasks.

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   1. UncheckedBioGPT scored 44.98%, 38.42% and 40.76% F1 on three relation extraction tasks and 78.2% accuracy on PubMedQA, which the authors call a new record.“Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA, creating a new record.”
   2. UncheckedIn a case study, BioGPT, a language model trained on biomedical literature, produced fluent descriptions of biomedical terms.“Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.”

2. Medicine › Artificial Intelligence in Healthcare and Education

   Can large language models reason about medical questions?

   Liévin, Hother, Geert and Ole · arXiv (Cornell University) · 2022

   The paper tests GPT-3.5, Llama-2 and other models on three medical question benchmarks using several prompting methods, and reports passing-level scores on all three datasets.

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   1. UncheckedAfter experts annotated step-by-step answers from InstructGPT, the authors report it can often read, reason and recall expert medical knowledge.“Based on an expert annotation of the generated CoTs, we found that InstructGPT can often read, reason and recall expert knowledge.”
   2. UncheckedWith few-shot prompts and ensembling, GPT-3.5 gave calibrated answer probabilities and reached passing scores on three medical question benchmarks.“Last, by leveraging advances in prompt engineering (few-shot and ensemble methods), we demonstrated that GPT-3.5 not only yields calibrated predictive distributions, but also reaches the passing score on three datasets: MedQA-USMLE 60.2%, MedMCQA 62.7% and PubMedQA 78.2%.”
   3. UncheckedLlama-2 70B, an open-source model, reached 62.5% accuracy on the MedQA-USMLE medical exam benchmark, which the paper counts as a pass.“Open-source models are closing the gap: Llama-2 70B also passed the MedQA-USMLE with 62.5% accuracy.”

3. Computer Science › Topic Modeling

   Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study

   Yang, Li, Zhou et al. · Journal of Medical Internet Research · 2025

   The authors propose LLM-Synergy, two ways of combining several language models, and report that both scored above the individual models on three medical question-answering datasets.

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   1. UncheckedA boosting-weighted majority vote of LLMs scored 35.84% on MedMCQA, 96.21% on PubMedQA and 37.26% on MedQA-USMLE, versus the best single model.“Specifically comparing the best individual LLM, the Boosting-based Majority Weighted Vote achieved accuracies of 35.84% on MedMCQA (+3.81%), 96.21% on PubMedQA (+0.64%), and 37.26% (tie) on MedQA-USMLE.”
   2. UncheckedA method that picks the best language model for each medical question scored 38.01% on MedMCQA, 96.36% on PubMedQA and 38.13% on MedQA-USMLE.“The Cluster-based Dynamic Model Selection yields even higher accuracies of 38.01% (+5.98%) for MedMCQA, 96.36% (+1.09%) for PubMedQA, and 38.13% (+0.87%) for MedQA-USMLE.”