ColPali: Efficient Document Retrieval with Vision Language Models

ColPali: Efficient Document Retrieval with Vision Language Models

The paper "ColPali: Efficient Document Retrieval with Vision Language Models" by Manuel Faysse, Hugues Sibille, Tony Wu, Gautier Viaud, Céline Hudelot, and Pierre Colombo proposes a novel approach to document retrieval that leverages Vision Language Models (VLMs) to enhance the retrieval process purely from document images. This work addresses the limitations of conventional text-centric retrieval systems that struggle with visually rich documents, incorporating visual cues for improved efficacy.

Introduction

Document retrieval involves matching user queries to relevant documents within a corpus. Traditional methods rely predominantly on text embedding models, which although effective, suffer from performance bottlenecks due to complex data ingestion pipelines. These pipelines involve extracting text from PDF documents using OCR, detecting document layouts, segmenting text into coherent chunks, and sometimes generating captions for visual elements.

Contributions

The paper makes two principal contributions:

1. ViDoRe Benchmark: The authors introduce the Visual Document Retrieval Benchmark (ViDoRe), which evaluates retrieval systems on page-level document retrieval across various domains, visual elements, and languages. ViDoRe highlights the limitations of text-centric models in visually rich settings and provides a comprehensive evaluation dataset.
2. ColPali Model: The authors propose ColPali, a novel retrieval model architecture that uses VLMs to produce high-quality contextualized embeddings directly from images of document pages. ColPali integrates a late interaction matching mechanism, substantially outperforming existing retrieval pipelines in terms of both performance and speed.

Methodology

ViDoRe Benchmark

ViDoRe is designed to evaluate multi-modal retrieval tasks involving text, figures, infographics, and tables across different thematic domains like medical, business, scientific, administrative, and in multiple languages (e.g., English, French). The benchmark includes:

• Academic Tasks: Subsets of widely-used visual question-answering datasets repurposed for retrieval tasks.
• Practical Tasks: Custom-built datasets targeting real-world retrieval applications using web-crawled PDF documents and queries generated via sophisticated AI methods and human validation.

ColPali Architecture

ColPali capitalizes on multi-modal capabilities by leveraging pre-trained VLMs like PaliGemma-3B. The model generates embeddings for both text and visual content, aligning them within a common latent space. The architecture incorporates late interaction mechanisms, where query and document embeddings interact at multiple levels to compute relevance scores efficiently.

Results

Performance

ColPali demonstrates superior performance across all tasks in the ViDoRe benchmark. The model exhibits substantial improvements in retrieval tasks involving visually rich content, such as figures, tables, and infographics. The late interaction architecture enables ColPali to perform complex query-document matching efficiently, outperforming conventional retrieval systems that incorporate visual elements through costly OCR and captioning processes.

Numerical Results

The ColPali model achieved the highest scores on the benchmark with significant margins. For instance, ColPali demonstrated a notable improvement in NDCG@5 metrics across various datasets, with increases of 22.6% on ArxivQA, 24.5% on DocVQA, and 29.1% on Energy datasets. These results highlight the model's capability to handle a diverse range of retrieval tasks effectively.

Latency and Memory Footprint

ColPali offers considerable speed advantages during indexing, as it processes documents directly from their image representations, bypassing the lengthy preprocessing steps required by text-centric models. While the model's memory footprint is larger due to multi-vector storage, efficient compression techniques can mitigate this issue.

Implications and Future Directions

ColPali's methodology has practical implications for enhancing document retrieval systems in industries that depend on accurate information extraction from complex documents. This includes applications in legal, healthcare, and scientific research domains where documents often contain pivotal visual information.

Future developments might explore advanced image decomposition strategies, improved image patch resampling techniques, and hard-negative mining to further refine retrieval performance. There's also potential in integrating visual retrieval with query answering systems to create comprehensive Retrieval-Augmented Generation (RAG) capabilities directly from visual features.

Conclusion

The paper presents a robust step forward in leveraging Vision Language Models for document retrieval, demonstrating that incorporating visual elements can significantly enhance retrieval performance. The release of ViDoRe and ColPali sets a new benchmark for future research in multimodal document retrieval systems.