@inproceedings{eidt-feldhus-2026-simplifying,
    title = "Simplifying Outcomes of Language Model Component Analyses with {ELIA}",
    author = "Eidt, Aaron Louis  and
      Feldhus, Nils",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.9/",
    pages = "111--128",
    ISBN = "979-8-89176-382-1",
    abstract = "While mechanistic interpretability has developed powerful tools to analyze the internal workings of Large Language Models (LLMs), their complexity has created an accessibility gap, limiting their use to specialists. We address this challenge by designing, building, and evaluating ELIA (Explainable Language Interpretability Analysis), an interactive web application that simplifies the outcomes of various language model component analyses for a broader audience. The system integrates three key techniques {--} Attribution Analysis, Function Vector Analysis, and Circuit Tracing {--} and introduces a novel methodology: using a vision-language model to automatically generate natural language explanations (NLEs) for the complex visualizations produced by these methods. The effectiveness of this approach was empirically validated through a mixed-methods user study, which revealed a clear preference for interactive, explorable interfaces over simpler, static visualizations. A key finding was that the AI-powered explanations successfully bridged the knowledge gap for non-experts; a statistical analysis showed no significant correlation between a user{'}s prior LLM experience and their comprehension scores, indicating that the system effectively leveled the playing field. We conclude that an AI system can indeed simplify complex model analyses, but its true power is unlocked when paired with thoughtful, user-centered design that prioritizes interactivity, specificity, and narrative guidance."
}