![]() (See our paper on PaperQA) PaperQA reduces hallucinations, provides context and references for how an answer was generated, is orders of magnitude faster than humans, and retains accuracy on par with experts. WikiCrow is built on top of PaperQA, a Retrieval-Augmented Generative (RAG) agent that, in our testing, can answer questions over the scientific literature better than other LLMs and commercial products. ![]() For more technical details, read on: PaperQA as a Platform for WikiCrow Make sure to check any information you read here yourself before relying on it, and please alert us to any errors you may find. On the other hand, WikiCrow is much better at providing citations than human authors. We expect that these errors will become rarer as the underlying models and techniques improve. You may also see repetitive statements, or citations that aren’t correct. The results are as follows:Īs you read WikiCrow articles, you will see incorrect statements about 9% of the time. We then repeated the same process for human-written articles. Is the statement correct according to the citation? Does the cited literature contain the information that is presented in the statement being evaluated?Īll statements were thus characterized as either having irrelevant or missing citations being cited and correct or being cited and incorrect.Is the statement cited? Is there a nearby citation that is clearly intended to support this statement, and is the citation relevant?.To evaluate WikiCrow, we randomly selected 100 statements and asked: By contrast, WikiCrow wrote all 15,616 articles in a few days (about 8 minutes per article, with 50 instances running in parallel), drawing on 14,819,358 pages from 871,000 scientific papers that it identified as relevant in the literature. We estimate that this task would have taken an expert human ~60,000 hours total (6.8 working years). ![]() As a first demo, we used WikiCrow to generate drafts of Wikipedia-style articles for all 15,616 of the Human protein-coding genes that currently lack articles or have stubs, using information from full-text articles that we have access to through our academic affiliations. WikiCrow is a first step towards automated synthesis of human scientific knowledge. To find out about genes like MGAT5B and ADGRA3, you’d end up sinking hours into reading the primary literature. ![]() This is part of a much broader problem today: scientific knowledge is hard to access, and often locked up in impenetrable technical reports. Often, plenty is known about the gene, but no one has taken the time to write up a summary. However, according to our count, only 3,639 of the 19,255 human protein-coding genes recognized by the HGNC have high-quality (non-stub) summaries on Wikipedia the other 15,616 lack pages or are incomplete stubs. Researchers turn to tools like Google, Uniprot or Wikipedia to learn more, as the knowledge of 20,000 human genes is too broad for any single human to understand. Experiments in genomics uncover lists of genes implicated in a biological process, like MGAT5B and ADGRA3. If you’ve spent time in molecular biology, you have probably encountered the “alphabet soup” problem of genomics. WikiCrow will be a foundational tool for the AI Scientists we plan to build in the coming years, and will help us to democratize access to scientific research. WikiCrow creates articles in 8 minutes, is much more consistent than human editors at citing its sources, and makes incorrect inferences or statements about 9% of the time, a number that we expect to improve as we mature our systems. WikiCrow is built on top of Future House’s internal LLM agent platform, PaperQA, which in our testing, achieves state-of-the-art (SOTA) performance on a retrieval-focused version of PubMedQA and other benchmarks, including a new retrieval-first benchmark, LitQA, developed internally to evaluate systems retrieving full-text PDFs across the entire scientific literature.Īs a demonstration of the potential for AI to impact scientific practice, we use WikiCrow to generate draft articles for the 15,616 human protein-coding genes that currently lack Wikipedia articles, or that have article stubs. Here, we present WikiCrow, an automated system that can synthesize cited Wikipedia-style summaries for technical topics from the scientific literature. Yet current LLMs are limited by hallucinations, lack access to the most up-to-date information, and do not provide reliable references for statements. The ability of large language models to comprehend and summarize natural language will transform science by automating the synthesis of scientific knowledge at scale. However, the scientific literature is so expansive that synthesis, the comprehensive combination of ideas and results, is a bottleneck. Scientific progress requires curation and synthesis of prior knowledge and experimental results. As scientists, we stand on the shoulders of giants.
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