Explaining the Reasoning of Large Language Models Using Attribution Graphs

This article, sourced from ArXiv, focuses on the interpretability of Large Language Models (LLMs). It proposes a method using attribution graphs to understand the reasoning process within these complex models. The core idea is to visualize and analyze how different parts of the model contribute to a specific output. This is a crucial area of research as it helps to build trust and identify potential biases in LLMs.