# Joint Extraction Matters: Prompt-Based Visual Question Answering for Multi-Field Document Information Extraction

Source: [Joint Extraction Matters: Prompt-Based Visual Question Answering for Multi-Field Document Information Extraction](<https://www.sota2.com/research/paper/68fa77951e209131cea7425d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:22:38.768Z

Authors: Mengsay Loem, Taiju Hosaka

Published: 2025-03-21T05:54:42.000Z

## Abstract

Visual question answering (VQA) has emerged as a flexible approach for extracting specific pieces of information from document images. However, existing work typically queries each field in isolation, overlooking potential dependencies across multiple items. This paper investigates the merits of extracting multiple fields jointly versus separately. Through experiments on multiple large vision language models and datasets, we show that jointly extracting fields often improves accuracy, especially when the fields share strong numeric or contextual dependencies. We further analyze how performance scales with the number of requested items and use a regression based metric to quantify inter field relationships. Our results suggest that multi field prompts can mitigate confusion arising from similar surface forms and related numeric values, providing practical methods for designing robust VQA systems in document information extraction tasks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.16868>)

[PDF](<https://arxiv.org/pdf/2503.16868.pdf>)