The Trade-offs of Document Representation

Multimodal Document Question Answering (QA) systems currently rely on three primary input strategies: processing raw document pixels (vision-only), using OCR-extracted text (text-only), or combining both (hybrid). This research investigates how these representations influence model performance, specifically looking at how well models interpret spatial layout versus semantic content.

Key Findings on Input Modalities

• Pixel-based representations excel at capturing complex spatial relationships, such as tables, charts, and non-linear document flows, but are computationally expensive and prone to resolution-related information loss.
• Text-based representations are highly efficient and leverage the strong reasoning capabilities of LLMs, but they often lose critical structural context (e.g., column alignment, visual hierarchy) that is essential for accurate document interpretation.
• Hybrid approaches typically provide the best performance by grounding semantic text in visual spatial context, though they introduce significant challenges in token length management and cross-modal alignment.

Practical Implications for Model Architecture

The study suggests that the optimal representation strategy is highly dependent on the document type. For dense, text-heavy documents, text-only or text-heavy hybrid models outperform vision-only models. Conversely, for documents where visual layout is the primary carrier of information (like invoices or scientific posters), pixel-based or vision-heavy hybrid models are necessary to maintain accuracy. The authors emphasize that developers must balance the high latency of visual encoders against the potential loss of structural intelligence when opting for text-only pipelines.