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About this paper
Sizhe Cheng, Jiaping Li, Huanchen Wang, Yuxin Ma
RAGTrace is an interactive visual evaluation system developed at Southern University of Science and Technology (SUSTech) that enables comprehensive diagnosis, understanding, and iterative optimization of Retrieval-Augmented Generation (RAG) workflows, offering an integrated view of retrieval and generation dynamics. It helps users analyze knowledge integration, diagnose retrieval errors, and refine retrieval strategies, validated through case studies and a user study.
Key insights
- A formative study with 12 experts revealed critical unmet needs, including the pervasive concern of 'hallucination,' the 'black box' nature of RAG, and the demand for systematic, iterative development and collaborative refinement.
- The system's design emphasizes multi-level error analysis (global, local, detailed, relational) and transparent attribution, clearly linking output components to specific retrieved documents and reasoning steps.
- RAGTrace's diagnostic methodology offers specific, extensible metrics to quantify different failure patterns, providing a granular understanding of issues beyond high-level performance metrics.
Summary by alphaXiv.
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