Visualizing the Chain of Thought in Large Language Models

Abstract

This Visualization Viewpoints article explores how visualization helps uncover and communicate the internal chain-of-thought trajectories and generative pathways of large language models (LLMs) in reasoning tasks. As LLMs become increasingly powerful and widespread, a key challenge is understanding how their reasoning dynamics unfold, particularly in natural language processing (NLP) applications. Their outputs may appear coherent, yet the multistep inference pathways behind them remain largely hidden. We argue that visualization offers an effective avenue to illuminate these internal mechanisms. Moving beyond attention weights or token saliency, we advocate for richer visual tools that expose model uncertainty, highlight alternative reasoning paths, and reveal what the model omits or overlooks. We discuss examples, such as prompt trajectory visualizations, counterfactual response maps, and semantic drift flows, to illustrate how these techniques foster trust, identify failure modes, and support deeper human interaction with these systems. In doing so, visualizing the chain of thought in LLMs lays critical groundwork for transparent, interpretable, and truly collaborative human–AI reasoning.