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arXiv Machine Learning By Martino Ciaperoni, Sezer Kutluk, Benedetta Muscato, Marta Marchiori Manerba, Fosca Giannotti

Virgil: Navigating Explainability for Transformer-based Language Models

Read the original on arXiv Machine Learning →

Virgil is an interactive system designed to help practitioners and researchers navigate the growing but fragmented ecosystem of explainability tools for transformer-based language models. It provides a unified interface backed by a curated knowledge base, allowing users—including non-experts—to discover and compare different explainability tools. The tool aims to simplify access to these resources as transformer models are increasingly deployed in high‑stakes applications.

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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