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Research question answering requires accurate retrieval and contextual understanding of scientific literature.
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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GRAG: Graph Retrieval-Augmented Generation
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Large Language Models for Scientific Question Answering: An Extensive Analysis of the SciQA Benchmark. In European Semantic Web Conference . Springer, 199–217
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Retrieval-Augmented Generation Approach: Document Question Answering using Large Language Model
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BioRAG: A RAG-LLM Framework for Biological Question Reasoning
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REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
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Evaluation of Retrieval-Augmented Generation: A Survey
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Almanac—retrieval-augmented language models for clinical medicine
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Retrieval-augmented generation for ai-generated content: A survey
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Dense text retrieval based on pretrained language models: A survey
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