section_path: Interpretation/Explainability
section_name: Explainability
paragraph_text: Explainability
  
  A related concept is AI explainability, which focuses on understanding how an AI model arrives at a given result. Techniques such as partial dependency plots, SHAP (SHapley Additive exPlanations), and feature importance assessments allow researchers to visualize and understand the contributions of various input features to the model's predictions. These methods help ensure that AI models make decisions based on relevant and fair criteria, enhancing trust and accountability.
  
  By integrating these techniques, researchers and practitioners can gain deeper insights into the operations of LLMs, fostering trust and facilitating the responsible deployment of these powerful models.
  
  In another example, the authors trained small transformers on modular arithmetic addition. The resulting models were reverse-engineered, and it turned out they used discrete Fourier transform.