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WIDE method enhances cross-modal generative retrieval by addressing information asymmetry

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced WIDE (Wildcard Inference with Dynamic Expansion), a novel approach to enhance cross-modal generative retrieval. This method addresses the challenge of information asymmetry between modalities, such as text and images, which can lead to inaccurate rankings due to forced hallucination in autoregressive decoders. WIDE utilizes Adaptive Entropy Thresholding to set uncertainty boundaries and Asymmetry-aware Wildcard Decoding to replace deterministic identifiers with wildcards, dynamically broadening the search space. A final Blind-Spot Re-ranking step combines generation confidence with semantic similarity to evaluate the expanded candidates, demonstrating superior performance on the M-BEIR benchmark. AI

IMPACT This research could improve the accuracy and efficiency of cross-modal search systems by mitigating issues with information asymmetry.

RANK_REASON The cluster contains a research paper detailing a new method for generative retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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WIDE method enhances cross-modal generative retrieval by addressing information asymmetry

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The cluster contains a research paper detailing a new method for generative retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Teng Guo, Xin Wang, Jiayou Xu, Keying Zhou, Jifeng Shen, Haoxin Ruan ·

    WIDE: Wildcard Inference with Dynamic Expansion for Cross-Modal Generative Retrieval

    arXiv:2609.03554v1 Announce Type: cross Abstract: Generative retrieval has demonstrated significant success by unifying representation learning and search into a single sequence-to-sequence generation task. However, extending this paradigm to cross-modal retrieval reveals a criti…

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