Rethinking the Effectiveness of Contrastive Decoding in Mitigating Hallucinations in MLLMs
Multimodal large language models frequently describe objects that are not present in an image. Contrastive decoding has become a widely used inference-time remedy, and it is assumed to work by contrasting a normal forward pass against a hallucination-prone one so that hallucinated content is suppressed. However, whether the reported benchmark improvements actually arise from this mechanism has not been examined. Our key observation is that a correction which suppresses hallucination must depend on whether the model is hallucinating, and that this dependence can be measured directly inside the model. Following this idea, we analyse the internal computation of three contrastive decoding met...
| Authors | Arnav Bendre, Guneesh Gupta, Shreyansh Modi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 July 2026 |
| Last updated at source | 3 October 2026 |
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