ColPali: Efficient Document Retrieval with Vision Language Models
The paper introduces ColPali, a model enhancing document retrieval by leveraging visual cues. It outperforms existing systems, introducing ViDoRe benchmark for evaluation, showcasing superior performance and speed in visually rich document retrieval.
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The paper titled "ColPali: Efficient Document Retrieval with Vision Language Models" introduces a new retrieval model architecture called ColPali, designed to enhance document retrieval systems by efficiently leveraging visual cues from documents. The authors highlight the limitations of current systems in utilizing visual information effectively and propose ColPali as a solution. The model utilizes Vision Language Models to generate contextualized embeddings solely from images of document pages, outperforming existing document retrieval pipelines in terms of quality and speed. To evaluate the performance of current systems on visually rich document retrieval, the authors introduce the Visual Document Retrieval Benchmark (ViDoRe), which includes various page-level retrieval tasks across different domains, languages, and settings. ColPali, combined with a late interaction matching mechanism, demonstrates superior performance while being faster and end-to-end trainable. This research contributes to advancing the field of document retrieval by addressing the challenges posed by visually rich document structures.
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