Latent Semantic Manifolds in Large Language Models

1. 1Develop a mathematical framework to interpret LLM hidden states as points on a latent semantic manifold
2. 2Use the Fisher information metric to equip the manifold with a Riemannian metric
3. 3Partition the manifold into Voronoi regions corresponding to tokens
4. 4Apply this framework to analyze and improve LLMs' performance and interpretability

ML researchers and AI engineers on a team benefit from this research as it provides a new perspective on understanding LLMs' internal computations, enabling them to improve model performance and interpretability

💡 LLM hidden states can be interpreted as points on a latent semantic manifold, providing a new perspective on understanding LLMs' internal computations

🤖 Latent Semantic Manifolds in LLMs reveal geometric consequences of internal computations