2024
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions
EMNLP 2024main
Embeddings from Large Language Models (LLMs) have emerged as critical components in various applications, particularly for information retrieval. While high-dimensional embeddings generally demonstrate superior performance as they contain more salient information, their practical application is freq…