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arXiv · 2509.17359

MLLM-Driven Semantic Identifier Generation for Generative Cross-Modal Retrieval

Tianyuan Li·Lei Wang·Ahtamjan Ahmat·Yating Yang·Bo Ma·Rui Dong·Bangju Han
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Abstract

Generative cross-modal retrieval, which treats retrieval as a generation task, has emerged as a promising direction with the rise of Multimodal Large Language Models (MLLMs). In this setting, the model responds to a text query by generating an identifier corresponding to the target image. However, existing methods typically rely on manually crafted string IDs, clustering-based labels, or atomic identifiers requiring vocabulary expansion, all of which face challenges in semantic alignment or scalability.To address these limitations, we propose a vocabulary-efficient identifier generation framework that prompts MLLMs to generate Structured Semantic Identifiers from image-caption pairs. These identifiers are composed of concept-level tokens such as objects and actions, naturally aligning with the model's generation space without modifying the tokenizer. Additionally, we introduce a Rationale-Guided Supervision Strategy, prompting the model to produce a one-sentence explanation alongside each identifier serves as an auxiliary supervision signal that improves semantic grounding and reduces hallucinations during training.

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Tianyuan Li, Lei Wang, Ahtamjan Ahmat, Yating Yang, Bo Ma, Rui Dong, Bangju Han. 2025-09-22. MLLM-Driven Semantic Identifier Generation for Generative Cross-Modal Retrieval. https://arxiv.org/abs/2509.17359

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