Considering responsible AI

In terms of LLMs, transparency and explainability are paramount to building trust and ensuring accountability. The complex nature of these models often results in a black box scenario where the decision-making process is opaque and difficult for users to understand. Enhancing the explainability of LLM decisions involves several techniques, including the development of interpretable models and the integration of explanation interfaces.

One effective approach is the use of model-agnostic methods, such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), which provide insights into how different features influence the output of any machine learning model, regardless of its complexity. These tools can help demystify LLM behaviors by indicating which parts of input data are most influential in determining the output.

Moreover, embedding explainability into the model architecture itself, such as through attention...