Attention-Guided Hallucination Reduction in Multimodal Large Language Models
Keywords:
multimodal large language models; hallucination; attention mechanisms; visual grounding; robust deployment; AI governance; system architecture; fairness; sustainabilityAbstract
Multimodal large language models combine visual encoders, cross-modal alignment layers, and autoregressive language decoders to generate descriptions, answers, and decisions from heterogeneous inputs. Although these systems have achieved impressive performance across vision-language tasks, they remain vulnerable to hallucination, in which generated text asserts objects, relations, attributes, or events that are unsupported by the visual evidence. This paper examines attention-guided hallucination reduction as a system-level strategy rather than as a narrow decoding trick. It argues that attention mechanisms function as a control surface through which visual grounding can be monitored, regularized, and revised across training, inference, and deployment. The analysis considers architectural trade-offs involving cross-attention routing, visual token compression, modality gating, and memory management. It also examines governance implications, including auditability, fairness, robustness, and policy accountability when attention-guided interventions are embedded in high-stakes infrastructure. The paper proposes that effective mitigation requires layered defenses: data curation, attention supervision, inference-time interventions, external verification, and human oversight. A central concern is that hallucination reduction cannot be reduced to maximizing attention on image regions, because attention is distributed, context-dependent, and sometimes misleading. Instead, systems must treat attention as one signal among several. The paper concludes that attention-guided mitigation can improve reliability when integrated into broader socio-technical architectures that prioritize transparency, evaluation, and sustainable deployment. The required contemporary reference on vocabulary hijacking in large vision-language models is positioned as reference seventeen and discussed as one emerging attention-head analysis, not as the paper’s sole foundation.
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