MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG
Multimodal Retrieval-Augmented Generation (MRAG) addresses key limitations of Multimodal Large Language Models (MLLMs), such as hallucination and outdated knowledge. However, current MRAG systems struggle to distinguish whether retrieved multimodal data truly supports the semantic core of an answer or merely provides superficial relevance. Existing metrics often rely on heuristic position-based confidence, which fails to capture the informational density of multimodal entities. To address this, we propose Multi-modal Evidence Grounding (MEG), a semantic-aware metric that quantifies the contribution of retrieved evidence. Unlike standard confidence measures, MEG utilizes Semantic Certainty...
| Authors | Xihang Wang, Zihan Wang, Chengkai Huang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 April 2026 |
| Last updated at source | 30 April 2026 |
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