This page contains interactive charts for exploring how large language models represent truth. It accompanies the paper The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets by Samuel Marks and Max Tegmark.

To produce these visualizations, we first extract LLaMA-13B representations of factual statements. These representations live in a 5120-dimensional space, far too high-dimensional for us to picture, so we use PCA to select the two directions of greatest variation for the data. This allows us to produce 2-dimensional pictures of 5120-dimensional data. See this footnote for more details.1

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