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Computer Science > Machine Learning

arXiv:2502.07577 (cs)
[Submitted on 11 Feb 2025]

Title:Automated Capability Discovery via Model Self-Exploration

Authors:Cong Lu, Shengran Hu, Jeff Clune
View a PDF of the paper titled Automated Capability Discovery via
Model Self-Exploration, by Cong Lu and 2 other authors
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    Abstract:Foundation models have become general-purpose
    assistants, exhibiting diverse capabilities across numerous
    domains through training on web-scale data. It remains
    challenging to precisely characterize even a fraction of the full
    spectrum of capabilities and potential risks in any new model.
    Existing evaluation approaches often require significant human
    effort, and it is taking increasing effort to design ever harder
    challenges for more capable models. We introduce Automated
    Capability Discovery (ACD), a framework that designates one
    foundation model as a scientist to systematically propose
    open-ended tasks probing the abilities of a subject model
    (potentially itself). By combining frontier models with ideas
    from the field of open-endedness, ACD automatically and
    systematically uncovers both surprising capabilities and failures
    in the subject model. We demonstrate ACD across a range of
    foundation models (including the GPT, Claude, and Llama series),
    showing that it automatically reveals thousands of capabilities
    that would be challenging for any single team to uncover. We
    further validate our method's automated scoring with extensive
    human surveys, observing high agreement between model-generated
    and human evaluations. By leveraging foundation models' ability
    to both create tasks and self-evaluate, ACD is a significant step
    toward scalable, automated evaluation of novel AI systems. All
    code and evaluation logs are open-sourced at this https URL.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
          Computation and Language (cs.CL)
Cite as:  arXiv:2502.07577 [cs.LG]
          (or arXiv:2502.07577v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2502.07577
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Cong Lu [view email]
[v1] Tue, 11 Feb 2025 14:23:13 UTC (3,578 KB)
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