When comparing the performance of PPP with other parameter-efficient methods like LoRA and adapter tuning for controlled text generation, PPP demonstrates significant advantages. While LoRA and adapter tuning reduce the number of trainable parameters, they may not offer the same level of control over the generated text as PPP. PPP's ability to steer language model outputs using prompt embeddings trained with minimal data sets sets it apart in terms of efficiency and effectiveness in controlling text generation. Additionally, PPP's focus on prompt tuning for control commands provides a more direct and interpretable method for guiding the language model's outputs.