Unveiling Key Aspects of Fine-Tuning in Sentence Embeddings: A Representation Rank Analysis
This work addresses the problem of optimizing fine-tuning for sentence embeddings, which is incremental but provides practical insights for researchers and practitioners in natural language processing.
The study analyzed fine-tuning dynamics in sentence embeddings using representation rank, revealing significant changes in alignment, uniformity, and linguistic abilities between phases, and proposed a rank reduction strategy that improved performance and stability in five state-of-the-art methods.
The latest advancements in unsupervised learning of sentence embeddings predominantly involve employing contrastive learning-based (CL-based) fine-tuning over pre-trained language models. In this study, we analyze the latest sentence embedding methods by adopting representation rank as the primary tool of analysis. We first define Phase 1 and Phase 2 of fine-tuning based on when representation rank peaks. Utilizing these phases, we conduct a thorough analysis and obtain essential findings across key aspects, including alignment and uniformity, linguistic abilities, and correlation between performance and rank. For instance, we find that the dynamics of the key aspects can undergo significant changes as fine-tuning transitions from Phase 1 to Phase 2. Based on these findings, we experiment with a rank reduction (RR) strategy that facilitates rapid and stable fine-tuning of the latest CL-based methods. Through empirical investigations, we showcase the efficacy of RR in enhancing the performance and stability of five state-of-the-art sentence embedding methods.