Understanding and Mitigating Toxicity in Image-Text Pretraining
  Datasets: A Case Study on LLaVA
Authors: name: Karthik Reddy Kanjula, name: Surya Guthikonda, name: Nahid Alam, name: Shayekh Bin Islam

Pretraining datasets are foundational to the development of multimodal
models, yet they often have inherent biases and toxic content from the
web-scale corpora they are sourced from. In this paper, we investigate the
prevalence of toxicity in LLaVA image-text pretraining dataset, examining how
harmful content manifests in different modalities. We present a comprehensive
analysis of common toxicity categories and propose targeted mitigation
strategies, resulting in the creation of a refined toxicity-mitigated dataset.
This dataset removes 7,531 of toxic image-text pairs in the LLaVA pre-training
dataset. We offer guidelines for implementing robust toxicity detection
pipelines. Our findings underscore the need to actively identify and filter
toxic content - such as hate speech, explicit imagery, and targeted harassment
- to build more responsible and equitable multimodal systems. The
toxicity-mitigated dataset is open source and is available for further
research.