Virgil: Navigating Explainability for Transformer-based Language Models
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Abstract (arXiv)
Explainability for transformer-based language models is becoming crucial as these systems are deployed in high-stakes applications. As a result, the ecosystem of explainability tools is rapidly evolving, becoming richer, but also more fragmented and harder to navigate. To address this challenge, we present Virgil, an interactive system that lets practitioners and researchers, including non-experts, navigate explainability tools for transformer language models. Supported by a curated knowledge base, the system enables users to discover and compare explainability tools within a unified interface.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 8 references that are in the map. First posted on arXiv 26 Aug 2026.
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References · 7
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
- 2023Explainability for Large Language Models: A Surveycited by 333
- 2021Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Modelscited by 222
- 2024A Primer on the Inner Workings of Transformer-based Language Modelscited by 85
- 2024On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMscited by 25
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