Papers with sequence construction and

1 papers
TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration (2025.emnlp-main)

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Challenge: Multimodal in-context learning (ICL) is a key mechanism for harnessing the capabilities of large vision–language models.
Approach: They propose a transformer-based model with task-aware attention that dynamically configures ICL sequences.
Outcome: Experiments on five LVLMs and nine datasets show that TACO surpasses baselines across diverse ICL tasks.

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