Visualizing Large Language Models: A Brief Survey

Abstract

This paper explores the current landscape of visualizing large language models (LLMs). The main objective was
threefold. Firstly, we investigate how we can visualize LLMspecific techniques such as prompt engineering, instruction tuning, or guidance. Secondly, LLM causality, interpretability, and
explainability are examined through visualization. And finally, we
showcase the role of visualization in illuminating the integration
of multiple modalities. We are interested in discovering the
papers that present visualization systems instead of those that use
visualization to showcase a part of their work. Our survey aims
to synthesize the state-of-the-art in LLM visualization, offering
a compact resource for exploring future research avenues.