AnimatedLLM: Explaining LLMs with Interactive Visualizations

2025-12-14

arXiv.org

AI Summary

This work addresses the lack of intuitive visualizations of internal mechanisms in existing large language model (LLM) educational materials. To bridge this gap, we propose and implement an interactive, browser-based visualization system that dynamically renders layer-wise model states and attention patterns by leveraging precomputed execution traces from open-source Transformer models on carefully curated inputs. Our system constitutes the first purely front-end-driven interactive teaching tool for LLMs, integrating pedagogically designed inputs with fine-grained visualizations of internal model dynamics. This approach significantly enhances model interpretability and facilitates deeper learning. The platform is publicly accessible at https://animatedllm.github.io, offering effective support for natural language processing education.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLMs) are becoming central to natural language processing education, yet materials showing their mechanics are sparse. We present AnimatedLLM, an interactive web application that provides step-by-step visualizations of a Transformer language model. AnimatedLLM runs entirely in the browser, using pre-computed traces of open LLMs applied on manually curated inputs. The application is available at https://animatedllm.github.io, both as a teaching aid and for self-educational purposes.

Problem

• large language models
• natural language processing education
• model interpretability
• interactive visualization
• Transformer

Innovation

• Interactive Visualization
• Large Language Models
• Transformer
• In-Browser Execution
• Model Interpretability