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Topic: Biomedical Text Mining and Ontologies ×Clear all

5 claims from 2 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.”
   3. UncheckedThe authors report that BioGPT, their biomedical language model, beat earlier models on most of six biomedical language-processing tasks.“We evaluate BioGPT on six biomedical NLP tasks and demonstrate that our model outperforms previous models on most tasks.”

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

   Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification

   Chen, Li, Lu et al. · arXiv (Cornell University) · 2023

   The study compared ChatGPT-family models with simpler bag-of-words and fine-tuned BioBERT models on two biomedical text tasks, using over 10000 samples as proxies for clinical tasks.

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   1. UncheckedIn this study, fine-tuning smaller models beat prompting ChatGPT-family models on two basic biomedical language tasks.“Despite the excitement around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks remained the best strategy.”
   2. UncheckedA simple bag-of-words model scored about the same as the most complex ChatGPT-style prompting on two biomedical text tasks in this study.“The simple BoW model performed on par with the most complex LLM prompting.”