Identifying Failure Modes in Retrieval-Augmented Generation Under Document Corruption via Unified Evaluation Platforms
Abstract
Evaluating the quality of retrieval-augmented generation (RAG) and document reranking systems remains challenging due to the lack of scalable, user-centric, and multi-perspective evaluation tools. We introduce RankArena, a unified platform for comparing and analysing the performance of retrieval pipelines, rerankers, and RAG systems using structured human and LLM-based feedback as well as for collecting such feedback. RankArena supports multiple evaluation modes: direct reranking visualisation, blind pairwise comparisons with human or LLM voting, supervised manual document annotation, and end- Research goal: Can unified evaluation platforms like RankArena identify specific failure modes in retrieval-augmented generation pipelines under varying levels of document corruption? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.7/10.
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