Understanding and Mitigating Toxicity in Image-Text Pretraining
  Datasets: A Case Study on LLaVA
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.
68262cf62f7ad318b2bbb7d8
Karthik Reddy Kanjula
extracted_confirmed
2025-05-15T18:12:33.905Z
650bd036be6db1ec2139be92
https://cdn-avatars.huggingface.co/v1/production/uploads/650bd036be6db1ec2139be92/5fp2m2x8reRkYIkVnwm1d.png
Karthik
user
kkr5155
68262cf62f7ad318b2bbb7d9
Surya Guthikonda
68262cf62f7ad318b2bbb7da
Nahid Alam
68262cf62f7ad318b2bbb7db
Shayekh Bin Islam
Understanding and Mitigating Toxicity in Image-Text Pretraining
  Datasets: A Case Study on LLaVA
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.
A detailed analysis of toxicity in the LLaVA image-text pretraining dataset identifies common types of harmful content and proposes strategies to mitigate it, resulting in a refined open-source dataset.
LLaVA image-text pretraining dataset multimodal models toxicity toxicity-mitigated dataset hate speech explicit imagery targeted harassment robust toxicity detection pipelines