Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models
This addresses the challenge of interpreting LLM reasoning for researchers and developers, though it is incremental as it builds on existing visualization and analysis techniques.
The paper tackles the problem of understanding the reasoning behavior of large language models (LLMs) by introducing a visualization tool called landscape of thoughts (LoT), which effectively distinguishes model strengths, answer correctness, and tasks, and uncovers undesirable patterns like low consistency and high uncertainty.
Numerous applications of large language models (LLMs) rely on their ability to perform step-by-step reasoning. However, the reasoning behavior of LLMs remains poorly understood, posing challenges to research, development, and safety. To address this gap, we introduce landscape of thoughts (LoT), the first landscape visualization tool to inspect the reasoning trajectories with certain reasoning methods on any multi-choice dataset. We represent the textual states in a trajectory as numerical features that quantify the states' distances to the answer choices. These features are then visualized in two-dimensional plots using t-SNE. Qualitative and quantitative analysis with the landscape of thoughts effectively distinguishes between strong and weak models, correct and incorrect answers, as well as different reasoning tasks. It also uncovers undesirable reasoning patterns, such as low consistency and high uncertainty. Additionally, users can adapt LoT to a model that predicts the property they observe. We showcase this advantage by adapting LoT to a lightweight verifier that evaluates the correctness of trajectories. Empirically, this verifier boosts the reasoning accuracy and the test-time scaling effect. The code is publicly available at: https://github.com/tmlr-group/landscape-of-thoughts.