Evaluating Embedding Generalization: How LLMs, LoRA, and SLERP Shape Representational Geometry

This article likely discusses the performance of Large Language Models (LLMs) and techniques like Low-Rank Adaptation (LoRA) and Spherical Linear Interpolation (SLERP) in terms of how well their embeddings generalize. It focuses on the geometric properties of the representations learned by these models.