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ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs
Our results reveal that: (1) ChatGPT can efficiently produce high-quality training data for debiasing other LLMs; (2) data produced via our approach surpasses existing datasets in debiasing performance while also preserving internal knowledge of a pre-trained LLM; and (3) synthetic data exhibits generalizability across categories, effectively mitigating various biases, including intersectional ones.
Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
Text-to-image diffusion models have been adopted into key commercial workflows, such as art generation and image editing.
BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models
We thus enable seamless open-ended social bias testing of PLMs by domain experts through an automatic large-scale generation of diverse test sentences for any combination of social categories and attributes.
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