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Synthesizing scientific literature with retrieval-augmented language models

A specialized language model for answering scientific queries and synthesizing literature.

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Synthesizing scientific literature with retrieval-augmented language models

By Akari Asai, Jacqueline He, Rulin Shao, Weijia Shi, Amanpreet Singh, Joseph Chee Chang, Kyle Shih-Huang Lo, Luca SoldainiNature
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The paper introduces OpenScholar, a retrieval-augmented language model that assists scientists in synthesizing literature. It outperforms other large language models on a challenging multi-paper synthesis task and achieves citation accuracy comparable to human experts.

The model's data store, retriever, and self-feedback inference loop improve off-the-shelf language models.

Abstract

The paper introduces OpenScholar, a retrieval-augmented language model that assists scientists in synthesizing literature. It outperforms other large language models on a challenging multi-paper synthesis task and achieves citation accuracy comparable to human experts. The model's data store, retriever, and self-feedback inference loop improve off-the-shelf language models.

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language modelsscientific literature synthesisretrieval-augmented modelscitation accuracyhuman expert comparisonLarge Language ModelsRetrieval & RAGSemantic InteroperabilityContent Engineering
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