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

• URL: http://arxiv.org/abs/2505.06356v1
• Date: Fri, 09 May 2025 18:01:50 GMT
• Title: Understanding and Mitigating Toxicity in Image-Text Pretraining Datasets: A Case Study on LLaVA
• Abstract summary: This dataset removes 7,531 of toxic image-text pairs in the LLaVA pre-training dataset.<n>We offer guidelines for implementing robust toxicity detection pipelines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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