Virgil：为基于Transformer的语言模型实现可解释性导航
Virgil: Navigating Explainability for Transformer-based Language Models
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中文总结 AI 辅助
针对Transformer语言模型可解释性工具碎片化难导航的问题，提出交互式系统Virgil，依托知识库实现统一界面下工具的发现与比较，为相关人员提供可解释性导航支持。
中文摘要 AI 辅助
随着基于Transformer的语言模型被部署到高风险应用场景中，其可解释性正变得愈发关键。因此，可解释性工具生态系统正快速发展，虽日益丰富，但也变得碎片化且难以导航。为应对这一挑战，我们提出了Virgil，这是一个交互式系统，可供从业者、研究人员（包括非专家）导航基于Transformer的语言模型的可解释性工具。在精心整理的知识库支持下，该系统使用户能在统一界面内发现并比较各类可解释性工具。
英文摘要
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.
发表机构
- Scuola Normale Superiore（高等师范学院）
- University of Turin（都灵大学）
机构由 AI 辅助整理，请以论文原文为准。
