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Computer Science > Computation and Language

arXiv:2409.13740 (cs)
[Submitted on 10 Sep 2024 (v1), last revised 26 Sep 2024 (this
version, v2)]

Title:Language agents achieve superhuman synthesis of scientific
knowledge

Authors:Michael D. Skarlinski, Sam Cox, Jon M. Laurent, James D.
Braza, Michaela Hinks, Michael J. Hammerling, Manvitha Ponnapati,
Samuel G. Rodriques, Andrew D. White
View a PDF of the paper titled Language agents achieve superhuman
synthesis of scientific knowledge, by Michael D. Skarlinski and 8
other authors
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    Abstract:Language models are known to hallucinate incorrect
    information, and it is unclear if they are sufficiently accurate
    and reliable for use in scientific research. We developed a
    rigorous human-AI comparison methodology to evaluate language
    model agents on real-world literature search tasks covering
    information retrieval, summarization, and contradiction detection
    tasks. We show that PaperQA2, a frontier language model agent
    optimized for improved factuality, matches or exceeds subject
    matter expert performance on three realistic literature research
    tasks without any restrictions on humans (i.e., full access to
    internet, search tools, and time). PaperQA2 writes cited,
    Wikipedia-style summaries of scientific topics that are
    significantly more accurate than existing, human-written
    Wikipedia articles. We also introduce a hard benchmark for
    scientific literature research called LitQA2 that guided design
    of PaperQA2, leading to it exceeding human performance. Finally,
    we apply PaperQA2 to identify contradictions within the
    scientific literature, an important scientific task that is
    challenging for humans. PaperQA2 identifies 2.34 +/- 1.99
    contradictions per paper in a random subset of biology papers, of
    which 70% are validated by human experts. These results
    demonstrate that language model agents are now capable of
    exceeding domain experts across meaningful tasks on scientific
    literature.

          Computation and Language (cs.CL); Artificial Intelligence
Subjects: (cs.AI); Information Retrieval (cs.IR); Physics and Society
          (physics.soc-ph)
Cite as:  arXiv:2409.13740 [cs.CL]
          (or arXiv:2409.13740v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2409.13740
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Andrew White [view email]
[v1] Tue, 10 Sep 2024 16:37:58 UTC (5,488 KB)
[v2] Thu, 26 Sep 2024 15:27:08 UTC (4,537 KB)
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