Nugget-based Annotation Protocol and Tool For Evaluating Long-form Retrieval-Augmented Generation
Abstract
Retrieval-augmented generation (RAG) summarizes retrieved documents into a text passage that fulfills the information need expressed by the user. Such generated responses should faithfully distill the relevant information and provide sufficient attribution back to the source documents. Nugget-based evaluation was proposed for text summarization and has been adapted to evaluate RAG output in recent shared tasks such as 2024 TREC RAG, BioGen, and NeuCLIR tracks. However, annotating such detailed and nuanced information is complex and errorful. Multiple pieces of information need to be labeled, extracted, linked, and cross-referenced. In this work, we present an annotation protocol and tool tailored to collecting information for evaluating RAG systems. Our tool has four steps: nugget creation, nugget revision, document support assessment, and finally, nugget alignment. Each step aims to minimize the annotator's cognitive load, improving the efficiency and reliability.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.