Training a large language model yields a powerful tool, but its internal workings can often seem opaque. Simply achieving good performance on evaluation metrics doesn't fully explain how the model arrives at its outputs or guarantee its reliability in diverse situations. This chapter focuses on methods to look inside the "black box" and analyze the learned behaviors of these complex systems.

You will examine techniques for interpreting model decisions and identifying potential weaknesses. Specifically, this chapter covers:

• Visualizing attention maps to understand how the model weighs input tokens.
• Using probing tasks to assess the linguistic information captured in the model's hidden states.
• Analyzing the activation patterns of individual neurons or layers.
• Strategies for identifying and understanding common failure modes.

Understanding these analysis techniques helps in debugging models, building trust in their outputs, and guiding further improvements or alignment efforts.