用大模型自动生成视觉问答解释数据，效率提升20倍
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models
- 用大视觉语言模型+优化提示词自动生成VQA-NLE数据
- 生成速度比人工快20倍，质量接近人工标注水平
- 加入视觉提示可显著提升文本相关性，适合研究多模态解释
自然语言解释（NLE）旨在通过自然语言提供详细、易懂的说明，揭示大视觉语言模型（LVLM）的决策过程。现有创建视觉问答带自然语言解释（VQA-NLE）数据集的方法依赖人工标注，耗时且成本高。本研究提出一种新方法，利用LVLM高效生成高质量合成VQA-NLE数据集。通过评估合成数据，我们证明先进提示技术可生成优质VQA-NLE数据。结果表明，该方法生成速度最高可达人工标注的20倍，质量下降极小，表现稳健，接近人工标注水平。此外，引入视觉提示显著提升了文本生成的相关性。本研究为多模态自然语言解释数据的高效可靠生成提供了可行路径。
原文摘要 · Abstract (English)
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.
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