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WikiCrow | Future House

futurehouse.org · 454 words · saved by 1 readers

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

Paper: https://paper.wikicrow.ai Code: https://github.com/Future-House/paper-qa Today, we are announcing PaperQA2 , the first AI agent to achieve superhuman performance on a variety of different scientific literature search tasks. PaperQA2 is an agent optimized for retrieving and summarizing information over the scientific literature. PaperQA2 has access to a variety of tools that allow it to find papers, extract useful information from those papers, explore the citation graph, and formulate answers. PaperQA2 achieves higher accuracy than PhD and postdoc-level biology researchers at retrieving

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