The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs
Visual interventions provide a training-free way to reduce hallucinations in multimodal large language models.
ProofPaper ↗
Key points
- We investigate where visual interventions retain leverage and when they improve predictions.
- Across six checkpoints from three model families, we observe a persistent drop in intervention effect and define its operational onset for this edit as the Evidence Anchoring Boundary (EAB).
- Comparison with intermediate readouts shows that the edit can lose leverage before answer preferences stabilize.
- Based on these observations, we develop calibrated selective routing, which preserves the original prediction unless calibration supports an intervention for the question type.
Sources (1)
- [1]The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:26 AM
Visual interventions provide a training-free way to reduce hallucinations in multimodal large language models.
We investigate where visual interventions retain leverage and when they improve predictions.
Extractive summary: sentences quoted from the sources.
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