Large language models (LLMs) process vast amounts of information to produce impressive outputs, such as summarizing texts, answering questions, and classifying sentiment. However, their operation often feels like a black box. 🤔 How do they make predictions? Can we identify which input information is relevant to the model's final decision?
In her blog post, relAI PhD Molly Kennedy introduces various Explainable AI (XAI) approaches. These methods help us better understand LLMs by providing partial, task-specific insights into their behavior 👉To learn about those methods, do not miss her post: https://zuseschoolrelai.de/blog/xai-methods-for-llm-interpretability/
