Diversity Awareness
Diversity awareness in artificial intelligence focuses on mitigating biases and enhancing the fairness and inclusivity of AI systems by addressing the underrepresentation of diverse populations in data and models. Current research emphasizes developing metrics to quantify diversity in synthetic datasets and generated outputs, employing techniques like contrastive learning and diffusion models to improve diversity in generated content, and adapting large language models to better represent diverse linguistic and cultural contexts. This work is crucial for ensuring the responsible development and deployment of AI, preventing discriminatory outcomes, and promoting equitable access to AI benefits across diverse populations.
Papers
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control
Yilun Zhao, Zhenting Qi, Linyong Nan, Lorenzo Jaime Yu Flores, Dragomir Radev
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising
Rishabh Gupta, Venktesh V., Mukesh Mohania, Vikram Goyal