PubMedQA supplies 273k+ biomedical QA instances that require reasoning over research abstracts to produce yes/no/maybe answers.
Enhanced lstm for natural language inference
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Framing fake news classification as natural language inference and ensembling NLI models with BERT, plus transitivity rules, achieves 88.063% test accuracy in the WSDM 2019 challenge.
A sequential fine-tuning strategy for pre-trained language models reports modest accuracy gains of 4.7%, 0.99%, and 0.72% on semantic similarity, sequence labeling, and text classification tasks.
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.
citing papers explorer
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PubMedQA: A Dataset for Biomedical Research Question Answering
PubMedQA supplies 273k+ biomedical QA instances that require reasoning over research abstracts to produce yes/no/maybe answers.
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Fake News Detection as Natural Language Inference
Framing fake news classification as natural language inference and ensembling NLI models with BERT, plus transitivity rules, achieves 88.063% test accuracy in the WSDM 2019 challenge.
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To Tune or Not To Tune? How About the Best of Both Worlds?
A sequential fine-tuning strategy for pre-trained language models reports modest accuracy gains of 4.7%, 0.99%, and 0.72% on semantic similarity, sequence labeling, and text classification tasks.
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Bias in Large Language Models: Origin, Evaluation, and Mitigation
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.