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We are excited to share our latest research paper Retrieve, Annotate, Evaluate, Repeat — Leveraging Multimodal LLMs for Large-Scale Product Retrieval Evaluation. We introduce a novel approach to large-scale product retrieval evaluation using Multimodal Large Language Models (MLLMs). Evaluated on 20,000 examples, our method shows how MLLMs can help automate the relevance assessment of retrieved products, achieving levels of accuracy comparable to human annotators and enabling scalable evaluation
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