Contrastive decoding strategies are widely used to reduce hallucinations in multimodal large language models (MLLMs). These methods work by constructing contrastive samples to induce hallucinations and then suppressing them in the output distribution. However, this paper demonstrates that such approaches fail to effectively mitigate the hallucination problem. The performance improvements observed on POPE Benchmark are largely driven by two misleading factors: (1) crude, unidirectional adjustments to the model's output distribution and (2) the adaptive plausibility constraint, which reduces the sampling strategy to greedy search. To further illustrate these issues, we introduce a series of spurious improvement methods and evaluate their performance against contrastive decoding techniques. Experimental results reveal that the observed performance gains in contrastive decoding are entirely unrelated to its intended goal of mitigating hallucinations. Our findings challenge common assumptions about the effectiveness of contrastive decoding strategies and pave the way for developing genuinely effective solutions to hallucinations in MLLMs.

本研究解决了多模态大语言模型中幻觉问题的方法不足的不足之处，揭示了对比解码策略的局限性。研究显示，尽管在性能基准上看似有所提升，这些提升实际上源于误导性因素，而不是有效的幻觉抑制。结果挑战了对比解码策略的有效性假设，为开发更有效的幻觉解决方案开辟了新方向。

性能提升的幻觉：对比解码为何未能解决多模态幻觉问题