Retrieval-Augmented Multimodal Language Models for Reducing Factual Hallucinations
Keywords:
retrieval-augmented generation; multimodal language models; factual hallucination; grounding; information retrieval; governance; socio-technical systems; robustness; fairness; deploymentAbstract
Retrieval-augmented multimodal language models promise to reduce factual hallucinations by coupling generative inference with external evidence drawn from text, images, audio, video, structured databases, and domain-specific repositories. Yet the integration of retrieval into multimodal generation is not a simple corrective layer. It reshapes model architecture, data governance, latency budgets, storage infrastructure, evaluation practices, and organizational accountability. This paper presents a system-level analysis of retrieval-augmented multimodal language models as socio-technical infrastructures for factual grounding. It examines how cross-modal indexing, query formulation, evidence ranking, fusion, attribution, and refusal mechanisms interact with the probabilistic tendencies of large generative models. The discussion emphasizes structural trade-offs among accuracy, coverage, latency, cost, privacy, fairness, and sustainability. It also considers robustness under distribution shift, adversarial retrieval poisoning, stale knowledge, modality conflict, and degraded network conditions. Governance implications are analyzed through documentation regimes, audit trails, content provenance, access control, and policy constraints. Case illustrations from clinical decision support, public-sector information services, industrial maintenance, journalism verification, and educational tutoring demonstrate that retrieval augmentation is most effective when treated as an operational capability rather than a one-time model modification. The paper concludes that reducing factual hallucinations requires an integrated architecture of evidence acquisition, multimodal alignment, uncertainty communication, human oversight, and continuous evaluation. Without such integration, retrieval can create an illusion of grounding while displacing hallucination from generation to retrieval, ranking, or governance failures.
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