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Synthesizing Scientific Literature With Retrieval-Augmented Language Models by dal.aske.13 is a document available to read on EtoBox.
The article introduces OpenScholar, a retrieval-augmented language model designed to assist researchers in synthesizing scientific literature from a database of 45 million open-access papers. OpenScholar outperforms existing models like GPT-4o in citation accuracy and correctness on a new benchmark called ScholarQABench, which evaluates long-form responses across multiple scientific domains. The system
- Author
- dal.aske.13
- Language
- EN