Virgil帮用户在复杂解释工具中快速找到合适方案
Virgil: Navigating Explainability for Transformer-based Language Models
- 构建统一界面整合多种解释工具
- 支持非专家通过知识库发现工具
- 适合想快速上手解释方法的研究者
随着基于Transformer的语言模型在高风险场景中的广泛应用，其可解释性变得日益重要。然而，解释工具生态迅速发展，内容丰富却愈发分散和难以导航。为此，我们提出Virgil——一个交互式系统，使从业者与研究人员（包括非专家）能够便捷地探索和比较针对Transformer语言模型的解释工具。该系统依托一个精心整理的知识库，在统一界面中实现工具发现与对比，提升可解释性研究的效率与可及性。
原文摘要 · Abstract (English)
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.
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