To extend PPP for controlling more fine-grained attributes of generated text, we can introduce additional discriminators specialized in different attributes. These discriminators can focus on specific aspects like formality, tone, specificity, or domain relevance. By training prompt embeddings with multiple discriminators, the model can learn to generate text that aligns with various nuanced attributes. Additionally, incorporating reinforcement learning techniques can enable the model to optimize for multiple objectives simultaneously, allowing for more intricate control over the generated text's characteristics.