Reproduce “Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search” on OpenResearch
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About this paper
Lei Chen, Chen Ju, Xu Chen, Zhicheng Wang, Yuheng Jiao, Hongfeng Zhan, Zhaoyang Li, Shihao Xu and 8 more
Alibaba Group developed Pailitao-VL, a unified multi-modal retrieval system employing an absolute ID-recognition embedding paradigm and a hybrid listwise reranker for real-time industrial search. The system enhances instance-level retrieval granularity and achieves faster inference, leading to measurable Gross Merchandise Volume gains in online A/B tests.
Key insights
- Reforming multi-modal retrieval into an absolute ID-recognition task, rather than relative distance optimization, significantly boosts instance-level discriminability for precise identification.
- A hybrid listwise reranking strategy that separates local comparative ordering from global absolute relevance scoring effectively mitigates the computational burden of MLLMs while maintaining high precision.
- Advanced data curation by MLLM-based agents, which build and refine a billion-scale semantic prototype library, is crucial for training models robust to real-world data imperfections and achieving high-resolution instance clustering.
Summary by alphaXiv.
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