Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Structuring (arxiv.org)

Recent research has introduced an innovative approach to optimizing hyperbolic embeddings in hierarchical data structures by employing Large Language Models (LLMs) for hierarchy restructuring. Hyperbolic geometry facilitates efficient representation of data that is inherently hierarchical, making it vital for applications such as recommendation systems and computer vision. The study underscores that optimal hyperbolic embeddings benefit from a high branching factor and single inheritance, while existing embedding algorithms can tolerate variations in size and balance. The core of this research is a prompt-based method that enables LLMs to automatically reorganize hierarchies based on these criteria, leading to enhanced embedding quality. Experiments conducted across 16 diverse hierarchies demonstrated that LLM-restructured frameworks resulted in significantly improved hyperbolic embeddings, validated by various standard quality metrics. This advancement not only boosts the performance of machine learning applications but also offers explainability; knowledge engineers receive justifications for the reorganizations, empowering them to make informed decisions about data structuring. Overall, this study marks a pivotal step in leveraging AI to augment hierarchical data representation, enhancing the effectiveness of machine learning systems.