Document Question Answering (DocQA)

• Document Question Answering (DocQA) is a field that integrates machine reading comprehension and multi-modal retrieval to extract precise answers from diverse, complex documents.
• It employs methodologies such as incremental processing, retriever–reader–reranker pipelines, and layout-aware models to efficiently capture and reason over contextual evidence.
• DocQA frameworks underpin real-world applications in regulatory analysis, scientific inquiry, and business intelligence, demonstrating scalable performance across varied document types.

Document Question Answering (DocQA) refers to the family of machine reading comprehension (MRC) and information retrieval techniques that address the problem of extracting accurate, contextually grounded answers to natural language queries posed over complex documents. These documents may be long, visually or structurally rich (e.g., containing tables, figures, or multiple sections), and may demand reasoning over both text and non-text modalities. DocQA underpins a wide range of real-world applications, including information extraction from scientific articles, regulatory documents, contracts, historical archives, and business reports. The development of robust DocQA systems has required advances across neural LLMing, evidence retrieval, multi-modal reasoning, incremental processing, and scalable benchmarking.

1. Foundations and Problem Formulation

The DocQA task is classically formulated as follows: given a document $D$ (potentially multi-page, multi-modal, and unstructured), and a question $Q$, the system is required to output an answer $A$ that is either a text span (extractive QA), a generated summary (abstractive QA), a Boolean value, or, in structured settings, a table, snippet, or reference to a visual region. Fundamental to DocQA is the need to support open-ended queries that may cross sentence, section, or even document boundaries, potentially requiring the integration of disparate evidence. Documents are not restricted to plain text—they may include scanned images, tables, figures, hierarchical structure, or even layouts demanding spatial reasoning.

Historically, DocQA research drew from single-passage/question answering, then open-domain QA, and evolved to tackle document-level complexity including multi-hop reasoning, layout-aware understanding, and robust multi-modal integration. Benchmarks and datasets such as SQuAD, QASPER, DocVQA, JDocQA, MMDocRAG, and DocHop-QA have standardized evaluation, spurring progress in both English and multilingual contexts.

2. Architectures and Methodologies

DocQA systems employ a variety of architectural paradigms, which have evolved from text-only machine reading comprehension models to advanced multi-modal frameworks:

• Incremental Processing Models: Extensions to classic models such as DocQA support "incremental reading" by slicing documents into fixed-length segments processed sequentially. Mechanisms like global answer prediction and step transfer allow models to accumulate context and reason efficiently, incorporating early stopping criteria that enable the system to halt reading when enough evidence is gathered, thereby reducing computational overhead without sacrificing accuracy (Abnar et al., 2019).
• Retriever–Reader–Reranker Pipelines: Many systems adopt a modular pipeline: a sparse/dense retriever selects relevant document regions, a reader model (often Transformer or BERT-based) extracts candidate answer spans, and a reranker rescales answer confidence based on local context and, in recent work, inter-document relationships. Knowledge-aided approaches inject external knowledge graph edges at both retrieval and reranking stages to propagate semantic connections (question–document, document–document), improving recall and answer ranking (Zhou et al., 2020). Hierarchical retrieval frameworks first select documents for broad context, then passages for fine-grained answers, using hierarchical title structures and advanced negative sampling (e.g., In-Doc, In-Sec) to sharpen discriminative power (Liu et al., 2021).
• Joint Ranking Systems: Newer DocQA approaches break pipeline independence assumptions by jointly optimizing document and snippet (sentence/paragraph) ranking layers. By looping relevance signals between granularity levels, these models close the feedback gap, yielding notable improvements in snippet retrieval metrics while also reducing parameter count compared to monolithic transformer-based designs (Pappas et al., 2021).
• Recognition-Free and Layout-Aware Methods: For handwritten or visually complex documents, recognition-free techniques directly retrieve answer regions as image snippets, projecting both word images and text into a common embedding space. This removes OCR dependence, enhancing robustness for historical or noisy documents (Mathew et al., 2021). Datasets such as BoundingDocs introduce fine-grained spatial annotations, enabling model grounding in precise bounding boxes and facilitating robust layout-aware document QA (Giovannini et al., 6 Jan 2025).
• Multi-view and Hierarchical Indexing: Content-aware chunking leverages the intrinsic document structure (sections, subsections) in long documents, avoiding arbitrary splits that obscure context or truncate answer spans. Augmenting each content chunk with views such as raw text, LLM-generated keywords, and summaries boosts recall across diverse retrievers, outperforming fixed-length chunking by large margins (Dong et al., 23 Apr 2024). Hierarchical indices further integrate in-page multi-modal associations and cross-page topological dependencies, supporting multi-granularity retrieval and improved evidence aggregation across document scales and modalities (Gong et al., 1 Aug 2025).
• Multi-Modal, Logically-Structured Retrieval: With documents increasingly encompassing images, tables, and complex layouts, leading frameworks encode pages with vision-LLMs (VLMs), construct graph-based representations that reflect both semantic and logical inter-page connections, and employ graph traversal techniques for efficient, logic-aware retrieval. Retrieval-augmented methods with logic-aware scoring (integrating both surface similarity and question-dependent logical checks) then interface with LVLMs for answer generation, achieving consistent gains in both retrieval and QA accuracy (Wu et al., 6 Sep 2025).

3. Multi-Modality, Visual Reasoning, and Explainability

Realistic DocQA often requires the integration of textual, tabular, and visual information:

• Multi-modal Representations: Systems encode tables, images, and figures alongside text, either by learning cross-modal embeddings, transforming visual data into descriptions, or aligning semantic representations across modalities (e.g., by combining column/row headers with cell content for tabular information). Retrieval and answer generation are thus robust to the presence of non-textual content (Mishra et al., 2023, Wang et al., 21 Aug 2024, Han et al., 18 Mar 2025).
• Explainability via Visual Heatmaps: Self-explainable DocQA frameworks increasingly incorporate built-in relevance map generation, where a model learns to produce visual heatmaps that highlight contextually sufficient and representation-efficient document regions necessary for answer prediction. Training objectives include explicit minimality (small context) and sufficiency (answer support), aligning interpretability with performance, and offering human-verifiable justifications for predictions (Souibgui et al., 12 May 2025).
• Structured Output Formats: Beyond textual answers, DocQA now addresses structured output, including generating answer tables directly from long document contexts (DocTabQA). Table-based answers clarify relationships between data points and increase user comprehension, with two-stage frameworks aligning relevant segments to table scaffolds before hierarchical generation (Wang et al., 21 Aug 2024).

4. Efficiency, Scalability, and Early Stopping

Scaling DocQA to long documents and large corpora has prompted research into computational and reading efficiency:

• Incremental and Early Stopping Strategies: By incrementally processing document slices and learning when to halt reading, models reduce the required amount of context examined. Early stopping modules, formalized as auxiliary binary classifiers with custom losses scaling with over- or under-reading distance, can match full-context model accuracy while cutting processing effort by significant fractions (8%–15% less text read) (Abnar et al., 2019).
• Content-Aware Indexing and Plug-and-Play Enhancement: MC-indexing and similar approaches impose no training overhead, offering a plug-and-play stage before any retriever. By chunking based on document structure and integrating multi-view representations, they optimize for recall and answer scope retrieval, providing direct gains for dense and sparse retrievers alike (Dong et al., 23 Apr 2024).
• Low-Parameter Efficient Architectures: Models such as joint PDRMM can deliver competitive or superior snippet/document retrieval with orders of magnitude fewer trainable parameters than transformer-based alternatives, reducing both computational and deployment burden (Pappas et al., 2021).

5. Benchmarks, Datasets, and Evaluation Protocols

DocQA advancements are rooted in the development of diverse, realistic benchmarks:

• Complex and Multimodal Benchmarks: Datasets such as MMDocRAG assemble multi-page, cross-modal QA pairs with expert-annotated evidence chains (text and image “quotes”), enabling rigorous evaluation of retrieval, evidence integration, and multimodal answer interleaving. Metrics include quote selection F1, BLEU, ROUGE-L, and LLM-as-judge criteria measuring citation quality, reasoning logic, and text-image coherence (Dong et al., 22 May 2025).
• Multilingual and Layout-Aware Resources: JDocQA provides a Japanese DocQA set with bounding boxes for answer clues, multiple question types (yes/no, factoid, numerical, open-ended), and unanswerable queries. Assessments include both text-only and multimodal model evaluation and demonstrate the utility of unanswerable examples for reducing hallucination (Onami et al., 28 Mar 2024).
• Multi-hop and Cross-Document Reasoning: DocHop-QA introduces a large-scale multi-hop, multimodal QA suite, requiring reasoning over unlinked scientific documents via semantic similarity and layout-aware evidence assembly. Tasks range from index extraction (with spatial accuracy) to structured and generative answering, promoting models for both discriminative and generative paradigms (Park et al., 20 Aug 2025).

6. Real-World Applications and Open Challenges

Modern DocQA systems enable end-to-end processing of raw and complex documents across diverse scenarios: scientific PDF analysis, ESG report interrogation, historical and handwritten archive retrieval, and cross-modal evidence synthesis in regulatory, financial, and medical domains. They must handle diverse layouts, noise, document lengths, and modalities as well as reason efficiently over multi-granular evidence.

Persistent challenges include:

• Cross-Modal Integration: Accurately retrieving and reasoning over disparate evidence types—especially under the limited context windows of current LVLMs—remains difficult (Dong et al., 22 May 2025, Gong et al., 1 Aug 2025).
• Logical Reasoning and Graph Traversal: RAG-only methods may overlook logically essential but semantically distant information; logic-aware retrieval frameworks employing page graphs and multi-hop VLMs demonstrate marked improvements (Wu et al., 6 Sep 2025).
• Interpretability and Trust: Built-in visual explanations and spatial grounding are central for high-stakes or user-facing settings (Souibgui et al., 12 May 2025, Giovannini et al., 6 Jan 2025).
• Zero-/Few-Shot Robustness: QA framing offers resilience to noise, rapid adaptation to new document formats, and superior extraction of long, complex entities, outperforming token classification in challenging regimes (Lam et al., 2023).

Ongoing research focuses on unifying semantic and logical retrieval objectives, refining evidence selection and aggregation in multi-modal settings, and improving the fidelity and scalability of DocQA deployment. With continual advances in benchmarks, architectures, and evaluation, DocQA is set to further impact real-world document intelligence and automated knowledge extraction systems.