# Spatially Grounded Explanations in Vision Language Models for Document Visual Question Answering

Source: [Spatially Grounded Explanations in Vision Language Models for Document Visual Question Answering](<https://www.sota2.com/research/paper/68fa7a0a1e209131cea80bb2>)

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

Data updated: 2025-10-28T13:57:19.152Z

Authors: Maximiliano Hormaz\\'abal Lagos, H\\'ector Cerezo-Costas, Dimosthenis Karatzas

Published: 2025-07-15T20:05:25.000Z

## Abstract

We introduce EaGERS, a fully training-free and model-agnostic pipeline that (1) generates natural language rationales via a vision language model, (2) grounds these rationales to spatial sub-regions by computing multimodal embedding similarities over a configurable grid with majority voting, and (3) restricts the generation of responses only from the relevant regions selected in the masked image. Experiments on the DocVQA dataset demonstrate that our best configuration not only outperforms the base model on exact match accuracy and Average Normalized Levenshtein Similarity metrics but also enhances transparency and reproducibility in DocVQA without additional model fine-tuning.

## 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/2507.12490>)

[PDF](<https://arxiv.org/pdf/2507.12490.pdf>)