Where MLLMs Attend and What They Rely On: Explaining Autoregressive
  Token Generation
Multimodal large language models (MLLMs) have demonstrated remarkable
capabilities in aligning visual inputs with natural language outputs. Yet, the
extent to which generated tokens depend on visual modalities remains poorly
understood, limiting interpretability and reliability. In this work, we present
EAGLE, a lightweight black-box framework for explaining autoregressive token
generation in MLLMs. EAGLE attributes any selected tokens to compact perceptual
regions while quantifying the relative influence of language priors and
perceptual evidence. The framework introduces an objective function that
unifies sufficiency (insight score) and indispensability (necessity score),
optimized via greedy search over sparsified image regions for faithful and
efficient attribution. Beyond spatial attribution, EAGLE performs
modality-aware analysis that disentangles what tokens rely on, providing
fine-grained interpretability of model decisions. Extensive experiments across
open-source MLLMs show that EAGLE consistently outperforms existing methods in
faithfulness, localization, and hallucination diagnosis, while requiring
substantially less GPU memory. These results highlight its effectiveness and
practicality for advancing the interpretability of MLLMs. The code is available
at https://github.com/RuoyuChen10/EAGLE.
68d9eb0f0177a6054b013a3e
Ruoyu Chen
claimed_verified
2025-10-01T15:29:35.492Z
635e557ccf0289e13c4223e9
https://cdn-avatars.huggingface.co/v1/production/uploads/1667126742973-635e557ccf0289e13c4223e9.jpeg
Ruoyu Chen
user
RuoyuChen
68d9eb0f0177a6054b013a3f
Xiaoqing Guo
68d9eb0f0177a6054b013a40
Kangwei Liu
68d9eb0f0177a6054b013a41
Siyuan Liang
68d9eb0f0177a6054b013a42
Shiming Liu
68d9eb0f0177a6054b013a43
Qunli Zhang
68d9eb0f0177a6054b013a44
Hua Zhang
68d9eb0f0177a6054b013a45
Xiaochun Cao
Where MLLMs Attend and What They Rely On: Explaining Autoregressive
  Token Generation
Multimodal large language models (MLLMs) have demonstrated remarkable
capabilities in aligning visual inputs with natural language outputs. Yet, the
extent to which generated tokens depend on visual modalities remains poorly
understood, limiting interpretability and reliability. In this work, we present
EAGLE, a lightweight black-box framework for explaining autoregressive token
generation in MLLMs. EAGLE attributes any selected tokens to compact perceptual
regions while quantifying the relative influence of language priors and
perceptual evidence. The framework introduces an objective function that
unifies sufficiency (insight score) and indispensability (necessity score),
optimized via greedy search over sparsified image regions for faithful and
efficient attribution. Beyond spatial attribution, EAGLE performs
modality-aware analysis that disentangles what tokens rely on, providing
fine-grained interpretability of model decisions. Extensive experiments across
open-source MLLMs show that EAGLE consistently outperforms existing methods in
faithfulness, localization, and hallucination diagnosis, while requiring
substantially less GPU memory. These results highlight its effectiveness and
practicality for advancing the interpretability of MLLMs. The code is available
at https://github.com/RuoyuChen10/EAGLE.
EAGLE is a lightweight framework that explains token generation in multimodal large language models by attributing tokens to visual regions and quantifying the influence of language and perceptual evidence.
multimodal large language models MLLMs EAGLE autoregressive token generation perceptual regions language priors perceptual evidence objective function insight score necessity score greedy search spatial attribution modality-aware analysis disentanglement faithfulness localization hallucination diagnosis