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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.12014 |
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| _version_ | 1866912645871828992 |
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| author | He, Eric Gupta, Akash Liusie, Adian Raina, Vatsal Molenda, Piotr Chabra, Shirom Raina, Vyas |
| author_facet | He, Eric Gupta, Akash Liusie, Adian Raina, Vatsal Molenda, Piotr Chabra, Shirom Raina, Vyas |
| contents | Text--image retrieval is necessary for applications such as product recommendation. Embedding-based approaches like CLIP enable efficient large-scale retrieval via vector similarity search, but they are primarily trained on literal caption-like text--image pairs and often fail to capture abstract or persona-driven attributes common in product recommendation applications (e.g., ``a gift for a mother who loves gardening''). In contrast, state-of-the-art vision--language models (vLLMs) can align text with images in a flexible manner, but their limited context window prevents them from directly handling retrieval over large catalogs. We propose a framework that distills the preference rankings of a powerful vLLM into an embedding-based system, transferring its nuanced alignment abilities while maintaining the inference-time scalability of an embedding-based approach. Experiments on persona-driven product recommendation tasks demonstrate that our method significantly outperforms existing embedding-based baselines, providing an efficient solution for personalized text--image retrieval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embedding the Teacher: Distilling vLLM Preferences for Scalable Image Retrieval He, Eric Gupta, Akash Liusie, Adian Raina, Vatsal Molenda, Piotr Chabra, Shirom Raina, Vyas Information Retrieval Machine Learning Text--image retrieval is necessary for applications such as product recommendation. Embedding-based approaches like CLIP enable efficient large-scale retrieval via vector similarity search, but they are primarily trained on literal caption-like text--image pairs and often fail to capture abstract or persona-driven attributes common in product recommendation applications (e.g., ``a gift for a mother who loves gardening''). In contrast, state-of-the-art vision--language models (vLLMs) can align text with images in a flexible manner, but their limited context window prevents them from directly handling retrieval over large catalogs. We propose a framework that distills the preference rankings of a powerful vLLM into an embedding-based system, transferring its nuanced alignment abilities while maintaining the inference-time scalability of an embedding-based approach. Experiments on persona-driven product recommendation tasks demonstrate that our method significantly outperforms existing embedding-based baselines, providing an efficient solution for personalized text--image retrieval. |
| title | Embedding the Teacher: Distilling vLLM Preferences for Scalable Image Retrieval |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2510.12014 |