DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding
Summary: arXiv:2604.12812v1 Announce Type: new
Introduction
In the rapidly evolving landscape of artificial intelligence, understanding long documents remains a critical challenge for Multimodal Large Language Models (MLLMs). Recent advancements have shown that these models experience significant performance degradation when tasked with processing lengthy texts. This degradation primarily results from two key issues: a low Signal-to-Noise Ratio (SNR) and a scarcity of effective supervision. The implications of these challenges are profound, particularly in applications requiring precise information extraction from extensive materials.
Challenges in Long Document Understanding
The challenges that hinder the performance of MLLMs in long document understanding can be summarized as follows:
- Low Signal-to-Noise Ratio (SNR): Crucial evidence often lies buried within irrelevant pages, making it difficult for models to identify and extract pertinent information.
- Supervision Scarcity: Traditional datasets typically provide only final short answers, leading to a weak learning signal that limits the model’s ability to learn from long documents effectively.
Proposed Solution: DocSeeker
To address the aforementioned challenges, this paper introduces DocSeeker, a novel framework that employs a structured workflow of Analysis, Localization, and Reasoning. This approach aims to enhance the ability of MLLMs to understand and process long documents systematically.
Two-Stage Training Framework
DocSeeker’s training methodology consists of two crucial stages:
- Supervised Fine-Tuning: The initial phase involves fine-tuning the model using high-quality data generated through an efficient knowledge distillation strategy. This step ensures that the model is equipped with a robust foundation for understanding complex document structures.
- Evidence-aware Group Relative Policy Optimization: The second phase focuses on jointly optimizing for evidence localization and answer accuracy, providing the model with a comprehensive understanding of where to find relevant information within lengthy texts.
Innovative Strategies
In addition to the two-stage training framework, DocSeeker incorporates an innovative Evidence-Guided Resolution Allocation strategy. This strategy effectively mitigates memory constraints when training on multi-page documents, ensuring that the model can handle large volumes of information without significant performance loss.
Empirical Results
Extensive experiments conducted to evaluate DocSeeker demonstrate its superior performance on both in-domain and out-of-domain tasks. The results reveal that DocSeeker not only generalizes robustly from short-page training to ultra-long documents but also synergizes effectively with visual Retrieval-Augmented Generation systems. This compatibility serves as a solid foundation for implementing advanced AI solutions in document understanding.
Conclusion
In conclusion, DocSeeker represents a significant advancement in the field of long document understanding. By addressing the challenges of low SNR and supervision scarcity through innovative training methodologies and strategic frameworks, DocSeeker paves the way for more effective and accurate processing of lengthy textual data in various applications.