WikiCrow: Automating Synthesis of Human Scientific Knowledge

About: “As scientists, we stand on the shoulders of giants. Scientific progress requires curation and synthesis of prior knowledge and experimental results. However, the scientific literature is so expansive that synthesis, the comprehensive combination of ideas and results, is a bottleneck. The ability of large language models to comprehend and summarize natural language will  transform science by automating the synthesis of scientific knowledge at scale. Yet current LLMs are limited by hallucinations, lack access to the most up-to-date information, and do not provide reliable references for statements.

Here, we present WikiCrow, an automated system that can synthesize cited Wikipedia-style summaries for technical topics from the scientific literature. 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.

As 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. 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 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…(More)”.

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