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arXiv Machine Learning By Rodion Krjut\v{s}kov, Eduard Barbu, Nikos Sakkas, Sofia Yfanti

Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models

Read the original on arXiv Machine Learning →

The paper introduces the Explainability Assistant, an open‑source conversational XAI system designed to interpret complex energy consumption forecasting models. By leveraging large language model function‑calling, it achieves 94% intent‑parsing accuracy and supports flexible natural‑language interaction across different ML problem types without task‑specific fine‑tuning. A comparative evaluation with energy domain specialists shows improved usability and consistent task accuracy, with all experts preferring the conversational interface over a traditional XAI dashboard.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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