Virgil: Navigating Explainability for Transformer-based Language Models
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Mr. Virgil: Learning Multi-robot Visual-range Relative Localization
Ultra-wideband (UWB)-vision fusion localization has achieved extensive applications in the domain of multi-agent relative localization. The challenging matching problem between robots and visual detection renders existin…
Graph Neural NetworkPose EstimationExplainability of Vision Transformers: A Comprehensive Review and New Perspectives
Transformers have had a significant impact on natural language processing and have recently demonstrated their potential in computer vision. They have shown promising results over convolution neural networks in fundament…
Decision MakingEcco: An Open Source Library for the Explainability of Transformer Language Models
Our understanding of why Transformer-based NLP models have been achieving their recent success lags behind our ability to continue scaling these models. To increase the transparency of Transformer-based language models, …
Text GenerationExplainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a language model predicts the next token and generates content is not gene…
Autonomous DrivingFrom Understanding to Utilization: A Survey on Explainability for Large Language Models
Explainability for Large Language Models (LLMs) is a critical yet challenging aspect of natural language processing. As LLMs are increasingly integral to diverse applications, their "black-box" nature sparks significant …
Model Editing