Understanding and Mitigating Toxicity in Image-Text Pretraining Datasets: A Case Study on LLaVA

Karthik, Surya Guthikonda, Nahid Alam, Shayekh Bin Islam

Understanding and Mitigating Toxicity in Image-Text Pretraining Datasets: A Case Study on LLaVA: 3 upvotes on Hugging Face Daily Papers, #13 of 17 papers on 2025-05-15. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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