LLMs, LoRA, and Slerp Shape Representational Geometry of Embeddings (arxiv.org)

A recent study has analyzed the generalization capabilities of text embeddings generated by large language models (LLMs) compared to non-LLM encoders. This research is particularly significant for the AI/ML community as it investigates the impacts of adaptation techniques, such as LoRA (Low-Rank Adaptation), and model merging strategies like spherical linear interpolation (SLERP) on embedding effectiveness. By systematically evaluating various encoder configurations—ranging from non-LLM models to LLMs enhanced with LoRA and SLERP—the authors uncover valuable insights into how these methodologies affect the representational geometry of embeddings, specifically in numeric sequence clustering and classification. The findings reveal that while LLM-based embeddings excel at capturing complex numerical relationships, they are susceptible to issues of over-specialization due to task-focused adaptation techniques. Notably, the use of SLERP merging is shown to effectively preserve the underlying structure of LLMs while enhancing adaptability, leading to improved performance in clustering and robustness without losing task-specific advantages. This research thus underscores the importance of careful model adaptation strategies in developing AI capabilities that balance specialized performance with broad generalization, paving the way for more effective applications across varied AI tasks.