RankArena Multi-Perspective Evaluation for Hallucination Detection in Cross-Domain Retrieval Versus Single-Metric Baselines
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: To what extent does RankArena's multi-perspective evaluation framework improve the detection of hallucination failure modes in cross-domain retrieval tasks compared to single-metric baselines? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.7/10.
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