ColPali: Efficient Document Retrieval with Vision Language Models (2407.01449v3)

Abstract: Documents are visually rich structures that convey information through text, as well as tables, figures, page layouts, or fonts. While modern document retrieval systems exhibit strong performance on query-to-text matching, they struggle to exploit visual cues efficiently, hindering their performance on practical document retrieval applications such as Retrieval Augmented Generation. To benchmark current systems on visually rich document retrieval, we introduce the Visual Document Retrieval Benchmark ViDoRe, composed of various page-level retrieving tasks spanning multiple domains, languages, and settings. The inherent shortcomings of modern systems motivate the introduction of a new retrieval model architecture, ColPali, which leverages the document understanding capabilities of recent Vision LLMs to produce high-quality contextualized embeddings solely from images of document pages. Combined with a late interaction matching mechanism, ColPali largely outperforms modern document retrieval pipelines while being drastically faster and end-to-end trainable.

Overview

The paper "ColPali: Efficient Document Retrieval with Vision LLMs" (Faysse et al., 27 Jun 2024 ) presents a technically refined approach to document retrieval that directly leverages Vision LLMs (VLMs) for processing document images. This model eschews traditional text extraction pipelines involving OCR and layout analysis by directly embedding the visual content of document pages. The central innovation is the integration of a late interaction mechanism that aligns query and document image embeddings, which enables efficient and accurate retrieval of visually rich documents.

Contributions and Methodology

Visual Document Retrieval Benchmark (ViDoRe)

A key contribution of the paper is the introduction of the ViDoRe benchmark, which defines a comprehensive evaluation framework covering page-level retrieval tasks across multiple domains (e.g., academic, clinical, legal, and business contexts) and languages (including English and French). The dataset is curated to assess retrieval systems over various document types, such as tables, figures, infographics, and standard text pages. This benchmark explicitly challenges retrieval models to handle multimodal inputs that include complex visual layouts, underscoring the limitations of conventional text-centric retrieval architectures.

ColPali Retrieval Model

ColPali is architected to generate high-quality, contextualized embeddings solely from document images. The model leverages pre-trained VLMs (for example, networks akin to PaliGemma-3B) to consolidate textual and visual information into a common latent space. The embeddings facilitate a late interaction matching mechanism where query embeddings and document representations interact at several granularity levels. This approach not only reduces the dependency on preprocessing pipelines—like OCR and text segmentation—but also provides significant improvements in retrieval speed and performance.

Performance Metrics and Quantitative Results

Numerical Improvements

The performance evaluation in the ViDoRe benchmark shows that ColPali outperforms conventional retrieval pipelines significantly. The paper highlights improvements in NDCG@5 across several datasets:

• A 22.6% increase on the ArxivQA dataset
• A 24.5% improvement on the DocVQA dataset
• A 29.1% gain on the Energy dataset

These statistics demonstrate ColPali’s effectiveness in contexts where documents are dominated by visual content and diverse layouts. The late interaction mechanism is particularly instrumental in these gains, given its ability to dynamically compute relevance without the overhead of dedicated OCR preprocessing.

Latency and Memory Considerations

ColPali not only exhibits superior retrieval performance but also offers notable efficiency gains in terms of latency. By circumventing the multi-stage document preprocessing pipeline typical of text-centric models, it allows for faster indexing. However, this is achieved with an increased memory footprint due to multi-vector storage, a challenge that the authors address with efficient compression techniques. This balance between computational efficiency and performance enhancement is a strong point in ColPali’s design.

Technical Insights and Implications

VLM Integration and Late Interaction Matching

The integration of VLMs within a document retrieval framework addresses the intrinsic limitation of traditional methods when confronted with visually rich layouts. By producing embeddings directly from raw images, ColPali captures complex visual features such as typography, spatial organization, and embedded graphics that are routinely omitted in text-only strategies. The late interaction matching mechanism further aligns multi-modal representations by allowing rich interactions between the query and retrieved documents at multiple embedding levels—a process that is pivotal for high recall and precision in document retrieval.

Broad Applicability and Future Directions

The robust performance of ColPali, as evidenced by significant gains in NDCG scores, suggests its potential application in fields where document layouts are heterogeneous and visually complex. Sectors like legal, healthcare, and scientific publishing, where documents are not only textually dense but enriched with figures, tables, and other visual elements, stand to benefit greatly. Future improvements could explore:

• More refined image decomposition techniques to further enhance the granularity of visual feature extraction.
• Advanced image patch resampling and hard-negative mining strategies to augment the training process.
• Integration with Retrieval Augmented Generation (RAG) frameworks to create end-to-end systems that combine retrieval with generative query answering.

Conclusion

ColPali represents a significant methodological advancement in the field of document retrieval by leveraging the strengths of Vision LLMs and late interaction matching. Its ability to process documents directly from images allows it to sidestep the computationally expensive OCR and layout analysis tasks traditionally required, while delivering robust performance improvements as quantified by significant increases in NDCG@5 across multiple benchmark datasets. These contributions are particularly relevant for applications dealing with visually rich documents, cementing ColPali’s position as a technically robust model for modern retrieval systems.