Mutual Reinforcement Effect (MRE) is an emerging subfield at the intersection
of information extraction and model interpretability. MRE aims to leverage the
mutual understanding between tasks of different granularities, enhancing the
performance of both coarse-grained and fine-grained tasks through joint
modeling. While MRE has been explored and validated in the textual domain, its
applicability to visual and multimodal domains remains unexplored. In this
work, we extend MRE to the multimodal information extraction domain for the
first time. Nello specifico, we introduce a new task: Multimodal Mutual
Reinforcement Effect (M-MRE), and construct a corresponding dataset to support
this task. To address the challenges posed by M-MRE, we further propose a
Prompt Format Adapter (PFA) that is fully compatible with various Large
Vision-Language Models (LVLMs). Experimental results demonstrate that MRE can
also be observed in the M-MRE task, a multimodal text-image understanding
scenario. This provides strong evidence that MRE facilitates mutual gains
across three interrelated tasks, confirming its generalizability beyond the
textual domain.

Questo articolo esplora i giri e le loro implicazioni.

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