Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models
<|reference_start|>Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models: 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.<|reference_end|>
@article{irawan2024towards,
    title={Towards Efficient and Robust VQA-NLE Data Generation with Large
  Vision-Language Models},
    author={Patrick Amadeus Irawan, Genta Indra Winata, Samuel Cahyawijaya, Ayu
  Purwarianti},
    journal={arXiv preprint arXiv:2409.14785},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14785},
    primaryClass={cs.CL cs.CV}
}