Output Distribution

Output distribution analysis focuses on characterizing the probability distribution of a model's predictions, aiming to improve model reliability and understanding. Current research emphasizes using techniques like denoising diffusion models alongside transformers, and adapting statistical methods from physics to detect phase transitions in model behavior, particularly within large language models and other complex systems. This research is crucial for enhancing model evaluation (e.g., through model-centric frameworks), improving the accuracy of tasks like forced alignment and anomaly detection, and mitigating security risks posed by adversarial attacks.