Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models

TL;DR

This paper introduces a method using large vision-language models to generate high-quality VQA-NLE datasets efficiently, reducing reliance on costly human annotations while maintaining near-human quality levels.

Contribution

It proposes a novel synthetic data generation approach leveraging LVLMs with advanced prompting, significantly speeding up dataset creation for VQA-NLE tasks.

Findings

Achieves up to 20x faster data generation than human annotation

Maintains near-human quality with minimal decrease in qualitative metrics

Visual prompts improve relevance and quality of generated explanations

Abstract

Natural Language Explanation (NLE) aims to elucidate the decision-making process by providing detailed, human-friendly explanations in natural language. It helps demystify the decision-making processes of large vision-language models (LVLMs) through the use of language models. While existing methods for creating a Vision Question-Answering with Natural Language Explanation (VQA-NLE) datasets can provide explanations, they heavily rely on human annotations that are time-consuming and costly. In this study, we propose a novel approach that leverages LVLMs to efficiently generate high-quality synthetic VQA-NLE datasets. By evaluating our synthetic data, we showcase how advanced prompting techniques can lead to the production of high-quality VQA-NLE data. Our findings indicate that this proposed method achieves up to 20x faster than human annotation, with only a minimal decrease in qualitative metrics, achieving robust quality that is nearly equivalent to human-annotated data. Furthermore, we show that incorporating visual prompts significantly enhances the relevance of text generation. Our study paves the way for a more efficient and robust automated generation of multi-modal NLE data, offering a promising solution to the problem.