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OpenScholar is an LLM that performs scientific literature reviews using a database of 45 million open-access articles.Credit: dpa via Alamy<\/p>\n
Researchers have published the recipe for an artificial-intelligence model that reviews the scientific literature better than some major large language models (LLMs) are able to, and gets the citations correct as often as human experts do.<\/p>\n
OpenScholar \u2014 which combines a language modelwith a database of 45 million open-access articles \u2014 links the information it sources directly back to the literature, to stop the system from making up or \u2018hallucinating\u2019 citations<\/a>.<\/p>\n Several commercial AI-based literature-review tools<\/a> already exist that use similar techniques, but few have been released as open source, says Akari Asai, an AI researcher at Carnegie Mellon University in Pittsburgh, Pennsylvania, and a co-author of the work, published in Nature on 4 February1<\/a>. Being open source means that researchers can not only try OpenScholar for free in an online demonstration<\/a>, but also deploy it on their own machine and use the method in the paper to boost the literature-review skills of any LLM, says Asai.<\/p>\n In the 14 months since OpenScholar was first published in the arXiv repository2<\/a>, AI firms such as OpenAI have used similar methods to tack \u2018deep research\u2019 tools<\/a> onto their commercial LLMs, which has greatly improved their accuracy. But as a small and efficient system, running OpenScholar costs a fraction of the price of using OpenAI\u2019s GPT-5 with deep research, co-author Hannaneh Hajishirzi, a computer scientist at the University of Washington in Seattle, tells the Nature podcast.<\/p>\n However, the authors acknowledge that OpenScholar has limitations. For example, it doesn\u2019t always retrieve the most representative or relevant papers for a query, and it is limited by the scope of its database.<\/p>\n But if researchers are able to access the tool for free, \u201cit can become one of the most popular apps for scientific searches,\u201d says Mushtaq Bilal, a researcher at Silvi, a Copenhagen-based firm that has its own AI-based literature-review tool.<\/p>\n Outperforming humans?<\/p>\n LLMs can write fluently, but they often struggle with citations.<\/a> This is because they learn by building links between words in their training data, which include sources outside science, and then generate text on the basis of probable associations that are not always correct or up to date. This is a feature of LLMs, not a bug, and it is proving to be a problem when people use LLMs in research. For example, at least 51 papers accepted to the high-profile machine learning NeurIPS conference in December 2025, contained non-existent or inaccurate citations, according to an analysis using the GPTZero tool<\/a>.<\/p>\n
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