Same evidence, different judgments: Evidence noncommutative in vision/speech-text conflicts
How input sequence order biases perception in multimodal AI models
For multimodal large language models, when images or speech conflict with accompanying text, measured text reliance can entangle modality preference with evidence position. Earlier studies of text bias often used a fixed evidence order or moved task instructions with the evidence...
ArXiv CS.AI
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This research reveals that the order in which a multimodal model encounters conflicting information significantly dictates its final judgment, highlighting a fundamental instability in how these systems process sensory inputs. Understanding these noncommutative effects is critical for developing more robust AI that can maintain consistent reasoning across diverse input modalities.
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