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Identifying Interactions at Scale for LLMs

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2026-07-16 19:08:00
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2026-07-16 19:08:00

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  • Blog / Newsletter2026-07-16 19:07:20
    Identifying Interactions at Scale for LLMs

    <!-- twitter --> <p style="text-align: center;"> <!-- <img src="https://bair.berkeley.edu/static/blog/spex/image1.png" alt="different_tests" width="600"><br> --> <img alt="different_tests" src="https://bair.berkeley.edu/static/blog/spex/image1.png" width="600" /><br /> <i style="font-size: 0.9em;"> </i> </p> <p>Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems through different lenses: <strong>feature attribution</strong>, which isolates the specific input features driving a prediction (<a href="https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html">Lundberg &amp; Lee, 2017</a>; <a href="