VaRS-Doc: Interpretation-Aware Variant Representations via Latent Self-Probing for Visual Document Retrieval
VaRS-Doc enhances visual document retrieval by diversifying document representations via latent self-probing, achieving state-of-the-art performance.
Key Findings
Methodology
VaRS-Doc framework diversifies document representations using latent self-probing. The model actively explores different latent interpretations during encoding, maintaining efficient late-interaction retrieval. A two-stage training strategy is employed: first, learning a single representation, then expanding to multiple representations, ensuring each branch effectively competes during training.
Key Results
- On ViDoRe V2 and V3 benchmarks, VaRS-Doc achieves NDCG@10 scores of 66.5% and 60.0%, significantly outperforming existing methods.
- Compared to ColQwen2.5, VaRS-Doc improves performance in economics and physics domains by 7.1% and 3.9%, respectively.
- Ablation studies show that the latent self-probing mechanism significantly enhances retrieval performance.
Significance
VaRS-Doc offers an innovative approach to visual document retrieval, addressing the mismatch between query-agnostic encoding and query-specific retrieval needs. It has significant impact in academia and industry, especially in applications requiring rapid processing of large document collections.
Technical Contribution
VaRS-Doc introduces a latent self-probing mechanism for diversified document representation, contrasting sharply with existing single representation methods. Its shared-contextualization with branched-encoding design is applied in visual document retrieval for the first time, offering new engineering possibilities.
Novelty
VaRS-Doc is the first to introduce latent self-probing in visual document retrieval, allowing document representations to adapt to different query intents, a significant innovation over traditional single representation methods.
Limitations
- In complex query scenarios, the model may still struggle to capture all potential interpretations, leading to decreased retrieval performance.
- Requires substantial computational resources for training, which may not be suitable for resource-constrained environments.
Future Work
Future work can explore applying VaRS-Doc on larger datasets and optimizing its computational efficiency. Additionally, researching how to apply this framework to other multimodal retrieval tasks is an interesting direction.
AI Executive Summary
Visual document retrieval is increasingly important in fields like enterprise search and scientific literature discovery. However, existing methods often use fixed document representations, which struggle to adapt to different query intents. VaRS-Doc enhances retrieval performance by diversifying document representations through latent self-probing.
The VaRS-Doc framework actively explores different latent interpretations during encoding, maintaining efficient late-interaction retrieval. Its two-stage training strategy ensures each branch effectively competes during training, preventing the model from relying on a single representation.
Experimental results show that VaRS-Doc achieves state-of-the-art performance on ViDoRe V2 and V3 benchmarks, with particularly strong results in economics and physics domains. Future work will explore applications on larger datasets and optimize computational efficiency.
Deep Analysis
Background
Visual document retrieval requires handling heterogeneous evidence, including paragraphs, tables, figures, and layout structures. Early methods relied on OCR-extracted text, losing important visual cues. Recently, multimodal large language models have advanced visual-textual semantic matching, providing a foundation for visual document retrieval.
Core Problem
Existing methods use fixed document representations, struggling to adapt to different query intents, limiting retrieval performance. Achieving diversified document representations without increasing online retrieval latency is a key challenge.
Innovation
VaRS-Doc diversifies document representations through latent self-probing. Its shared-contextualization with branched-encoding design allows the model to explore different latent interpretations during encoding, avoiding the limitations of traditional fixed representations.
Methodology
- �� Latent Self-Probing: Achieves diversified document representation through learnable probing tokens.
- �� Shared Contextualization and Branched Encoding: Shares early encoding blocks, branches later encoding blocks.
- �� Two-Stage Training: First learns a single representation, then expands to multiple representations.
Experiments
Experiments are conducted on ViDoRe V2 and V3 benchmarks, using NDCG@10 as the evaluation metric. Compared with various baseline models, VaRS-Doc's superiority is verified. Ablation studies analyze the contribution of the latent self-probing mechanism.
Results
VaRS-Doc outperforms existing methods across multiple domains, particularly in economics and physics. Ablation studies show that the latent self-probing mechanism significantly enhances retrieval performance.
Applications
VaRS-Doc can be used in enterprise search, scientific literature discovery, and other applications requiring rapid processing of large document collections. Its diversified representation capability makes it broadly applicable in multimodal retrieval tasks.
Limitations & Outlook
The model may struggle to capture all potential interpretations in complex query scenarios. Additionally, the training process requires substantial computational resources, which may not be suitable for resource-constrained environments. Future work can explore methods to optimize computational efficiency.
Plain Language Accessible to non-experts
Imagine you're in a library looking for a book. Traditional methods are like a librarian giving you a fixed introduction to a book, no matter what question you ask. VaRS-Doc is like a smart librarian who gives you different introductions based on your questions. This approach makes it easier for you to find the book you need.
ELI14 Explained like you're 14
Imagine you're playing a game with many levels, each with different challenges. VaRS-Doc is like a super helper that gives you different tips and help based on your current challenge, making it easier for you to pass the level. Isn't that cool?
Glossary
Latent Self-Probing
A mechanism allowing the model to explore different latent interpretations during encoding.
Used to achieve diversified document representation.
Late Interaction
An architecture where documents and queries are matched after independent encoding.
Used for efficient online retrieval.
Shared Contextualization
Shares early encoding blocks to provide general document context.
Used in the initial stage of latent self-probing.
Branched Encoding
Branches into independent later encoding after shared contextualization.
Used to generate diversified document representations.
Two-Stage Training
A training strategy that first learns a single representation, then expands to multiple representations.
Ensures each branch effectively competes during training.
Open Questions Unanswered questions from this research
- 1 How to apply VaRS-Doc on larger datasets remains to be explored.
- 2 Optimizing computational efficiency for resource-constrained environments is a future research direction.
Applications
Immediate Applications
Enterprise Search
VaRS-Doc can help enterprises quickly find relevant information from large document collections, improving efficiency.
Long-term Vision
Multimodal Retrieval
VaRS-Doc's diversified representation capability can play a significant role in multimodal retrieval tasks, advancing the field.
Abstract
Visual document retrieval has recently become increasingly important in applications such as enterprise search, scientific literature discovery, and retrieval-augmented generation. These applications depend on efficiently identifying query-relevant pages across large collections of visually rich documents. Existing methods commonly adopt late-interaction architectures that encode and index documents offline to enable scalable and low-latency online retrieval. Despite its efficiency, this paradigm requires each document to be encoded into a fixed representation before the query is known. However, the same content in a visual document may induce different interpretations depending on the query intent, which a fixed representation struggles to capture. Yet postponing document encoding until the query arrives would incur prohibitive online retrieval latency. To address this gap, we propose VaRS-Doc, a visual document retrieval framework that diversifies document representations by enabling the model to actively explore variant latent interpretations during document encoding, while preserving efficient late-interaction retrieval in which each query adaptively selects the best-fit representation. We further introduce a two-stage training strategy that encourages the model to capture complementary semantic interpretations and prevents it from falling back to train a single dominant representation. Experiments on visual document retrieval benchmarks show that VaRS-Doc achieves state-of-the-art retrieval performance, offering a practical solution to the mismatch between query-agnostic document encoding and query-specific retrieval needs. Code is available at https://github.com/bokufa/VaRS-Doc.