Visualizing LLM Thinking with Python Toolkit

A PhD student in Electromagnetics developed a Python toolkit to visualize the “thinking process” of Local LLMs by treating inference as a physical signal trajectory. This tool extracts hidden states layer-by-layer and presents them as 2D/3D trajectories, revealing insights such as the “Confidence Funnel,” where different prompts converge into a single attractor basin, and distinct “Thinking Styles” between models like Llama-3 and Qwen-2.5. Additionally, the toolkit visualizes model behaviors like “Refusal” during safety checks, offering a geometric perspective on model dynamics and safety tuning. This approach provides a novel way to profile model behaviors beyond traditional benchmarks.

Understanding the inner workings of large language models (LLMs) can be as elusive as interpreting the human thought process. The exploration of hidden states as dynamic flows through high-dimensional spaces offers a fresh perspective on how these models process information. By visualizing these states as trajectories, it becomes possible to gain insights into the models’ reasoning paths. This approach not only demystifies the “thinking process” of LLMs but also provides a tangible way to analyze and compare different models. Such visualization can reveal the underlying geometric shapes of thoughts, offering a new dimension to model evaluation beyond traditional benchmarks.

The concept of a “Confidence Funnel” highlights how models converge on a concept despite starting from varied prompts. This convergence into a single “attractor basin” suggests a level of consistency and robustness in the model’s reasoning process. It provides a visual representation of how models resolve ambiguity and arrive at a coherent understanding. This insight is crucial for developers and researchers aiming to refine model training and improve the consistency of outputs across different queries. Understanding this convergence can lead to more reliable AI systems that perform consistently across diverse inputs.

Comparing different models like Llama-3 and Qwen-2.5 through their “thinking styles” offers a fascinating glimpse into their architectural differences. Llama-3’s early decision-making contrasts with Qwen-2.5’s prolonged ambiguity, suggesting varied approaches to processing information. These differences in trajectory shapes can inform model selection based on specific use cases, such as whether a task requires quick decision-making or the ability to maintain multiple possibilities before concluding. This geometric profiling of models can enhance our understanding of their strengths and weaknesses, guiding more informed choices in AI deployment.

The visualization of refusal behaviors, such as “hard refusal” and “soft steering,” provides a novel way to assess and improve model safety. By treating these behaviors as geometric trajectories, developers can visually gauge the effectiveness of safety measures like Reinforcement Learning from Human Feedback (RLHF). This approach acts as a “Geiger Counter” for safety tuning, allowing for the identification of whether a model’s refusal mechanisms are too rigid or appropriately flexible. Such insights are invaluable for ensuring that AI systems adhere to ethical guidelines while maintaining user engagement and satisfaction.

Read the original article here