Intervening Anchor Token: Decoding Strategy in Alleviating Hallucinations for MLLMs

Multimodal large language models (MLLMs) offer a powerful mechanism for interpreting visual information. However, they often suffer from hallucinations, which impede the real-world usage of these models. Existing methods attempt to alleviate this issue by designing special decoding strategies that penalize the summary tokens. However, these methods lack analysis of the relationship between hallucination and the summarization mechanism of LLMs. Interestingly, we find that penalizing summary tokens is not necessary: merely intervening in the query-key parameters variance, without costing extra inference time, still alleviates hallucinations. Specifically, we explore the causes of hallucinations by analyzing localized self-attention patterns called "anchor" tokens and define the attention localization degree of the model as token propagation probabilities. Our analysis reveals that over-propagation of anchor tokens occurs when the distribution of eigenvalues of the query and key matrices has a non-zero mean and a polarized variance, leading to excessive dependence on anchor tokens while neglecting vision information and describing the image content with hallucination. Based on the observation, we propose a versatile plug-and-play decoding strategy, Dynamic Token Propagation Mechanism (TAME), to alleviate excessive propagation by dynamically intervening in the eigenspectrum variance of the attention weight, thereby alleviating hallucinations without relying on complex decoding strategies. Extensive experiments reveal a correlation between the eigenspectrum and hallucinations across various MLLMs and show that TAME reduces the percentage of hallucinated objects. Code released at https://github.com/Everlyn-Labs/ANTRP .