Model Criticism

Model criticism focuses on evaluating the quality and reliability of large language model (LLM) outputs, aiming to identify and correct errors or biases. Current research emphasizes developing LLM-based "critics" that provide fine-grained feedback, often leveraging reinforcement learning and techniques like neural posterior estimation to assess various aspects of model performance, including factual consistency, code correctness, and high-level structural coherence in long-form text. These advancements are crucial for improving LLM trustworthiness and facilitating their responsible deployment in diverse applications, ranging from code generation to scientific modeling.