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Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Can ChatGPT Replace Traditional KBQA Models? An In-Depth Analysis of the Question Answering Performance of the GPT LLM Family
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COBERT: COVID-19 Question Answering System Using BERT
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Biomedical question answering: A survey
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Publisher Correction: Large language models encode clinical knowledge
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Large language models in medicine
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Complementary and Integrative Health Information in the literature: its lexicon and named entity recognition
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PMC-LLaMA: toward building open-source language models for medicine
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RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition
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LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction
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Classifiers Combination Techniques: A Comprehensive Review
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Feature Selection for Cluster Analysis: an Approach Based on the Simplified Silhouette Criterion
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A Weighted Majority Voting Ensemble Approach for Classification
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New ensemble methods for evolving data streams
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Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
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Biomedical Question Answering: A Survey of Approaches and Challenges
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Pre-trained Language Models in Biomedical Domain: A Systematic Survey
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The Science of Detecting LLM-Generated Text
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A Survey on Evaluation of Large Language Models
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Question Answering in Restricted Domains: An Overview
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StereoSet: Measuring stereotypical bias in pretrained language models
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PubMedQA: A Dataset for Biomedical Research Question Answering
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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