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[2602.18262] Simplifying Outcomes of Language Model Component Analyses with ELIA
Summary
The paper presents ELIA, an interactive web application designed to simplify the analysis of Large Language Models (LLMs) for non-experts by providing natural language explanations of complex visualizations.
Why It Matters
As mechanistic interpretability tools for LLMs become increasingly complex, ELIA addresses the accessibility gap, enabling broader audiences to understand and utilize these analyses. This is crucial for democratizing AI technology and fostering informed discussions around LLM capabilities and limitations.
Key Takeaways
- ELIA integrates multiple analysis techniques to enhance understanding of LLMs.
- The application provides AI-generated natural language explanations for complex visual data.
- User studies indicate that interactive interfaces significantly improve comprehension for non-experts.
- The system effectively reduces barriers to understanding LLM analyses across varying experience levels.
- Thoughtful design prioritizing interactivity and narrative guidance is essential for effective communication of complex information.
Computer Science > Computation and Language arXiv:2602.18262 (cs) [Submitted on 20 Feb 2026] Title:Simplifying Outcomes of Language Model Component Analyses with ELIA Authors:Aaron Louis Eidt, Nils Feldhus View a PDF of the paper titled Simplifying Outcomes of Language Model Component Analyses with ELIA, by Aaron Louis Eidt and 1 other authors View PDF HTML (experimental) 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 helped bridge the knowledge gap for non-experts; a statistical analysis showed no sign...
