VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents (2410.10594v1)

Abstract: Retrieval-augmented generation (RAG) is an effective technique that enables LLMs to utilize external knowledge sources for generation. However, current RAG systems are solely based on text, rendering it impossible to utilize vision information like layout and images that play crucial roles in real-world multi-modality documents. In this paper, we introduce VisRAG, which tackles this issue by establishing a vision-LLM (VLM)-based RAG pipeline. In this pipeline, instead of first parsing the document to obtain text, the document is directly embedded using a VLM as an image and then retrieved to enhance the generation of a VLM. Compared to traditional text-based RAG, VisRAG maximizes the retention and utilization of the data information in the original documents, eliminating the information loss introduced during the parsing process. We collect both open-source and synthetic data to train the retriever in VisRAG and explore a variety of generation methods. Experiments demonstrate that VisRAG outperforms traditional RAG in both the retrieval and generation stages, achieving a 25--39\% end-to-end performance gain over traditional text-based RAG pipeline. Further analysis reveals that VisRAG is effective in utilizing training data and demonstrates strong generalization capability, positioning it as a promising solution for RAG on multi-modality documents. Our code and data are available at https://github.com/openbmb/visrag .

Vision-based Retrieval-augmented Generation on Multi-modality Documents: A Synopsis

The paper "VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents" proposes a sophisticated framework designed to enhance retrieval-augmented generation (RAG) by integrating vision-LLMs (VLMs). This approach addresses limitations in traditional text-based RAG systems that neglect multimodal aspects such as layout and images, which are prevalent in real-world documents.

Methodology

The core of VisRAG lies in bypassing the document parsing stage typical in traditional text RAG frameworks, opting instead to directly embed document pages as images using VLMs. The pipeline primarily consists of two components: VisRAG-Ret for retrieval and VisRAG-Gen for generation.

1. Retrieval (VisRAG-Ret):
   • Utilizes a dual-encoder setup, akin to dense retrieval models, but employs a VLM to encode both the query as text and the document as an image.
   • Embeddings are generated through position-weighted mean pooling, ensuring comprehensive representation.
   • The model is optimized using the InfoNCE loss, facilitating retrieval based on cosine similarity.
2. Generation (VisRAG-Gen):
   • Transforms retrieved top-$k$ pages into answers using multiple proposed methods:
     • Page Concatenation: Combines images for VLMs limited to single-image inputs.
     • Weighted Selection: Ranks generated answers from individual pages by weighted confidence, ideal for mitigating noise from irrelevant documents.
     • Multi-image VLMs: Leverages advanced VLMs capable of multi-image input to potentially improve generation accuracy.

Experimental Results

The paper's experiments highlight VisRAG's superiority over existing RAG methods:

• Retrieval Performance: The VisRAG-Ret outperforms baseline models across various datasets, demonstrating significant improvements even against state-of-the-art text and vision models. Notably, its capability extends to synthesizing robust document embeddings, achieving higher training data efficiency and demonstrating superior out-of-domain generalization capabilities.
• Generation Performance: VisRAG-Gen effectively utilizes visual and textual information, leading to higher accuracy in generating answers compared to text-based generators. The most advanced multi-image VLMs exhibit increasing accuracy with additional retrieved documents, underscoring the benefits of cross-image reasoning.
• End-to-end Performance: Combining VisRAG-Ret and VisRAG-Gen results in a substantial end-to-end performance gain, with notable improvements in retrieval accuracy and answer generation fidelity over traditional TextRAG pipelines.

Implications and Future Directions

The development of VisRAG presents a paradigm shift in handling multi-modality documents within RAG systems. By directly leveraging the full spectrum of information available in original documents, VisRAG mitigates information loss common to text-based methods. This advancement paves the way for more accurate and contextually aware systems across various applications, particularly where images and layout play a crucial role.

The implications extend into practical deployments within areas such as document analysis, contract management, and educational tools, where blended document formats are standard. Theoretically, integrating VLMs emphasizes the potential for deeper semantic understanding across modalities, redefining how information is retrieved and synthesized.

Future research could explore scaling VisRAG across broader document corpora, optimizing multi-image VLMs for complex reasoning tasks, and integrating emergent VLM techniques. There is also an opportunity to refine retrieval and generation strategies to accommodate increasingly diverse and intricate document structures.