Does Size Matter? Examining Sentence Similarity Performance in Large Language Models

Sentence embeddings are a crucial component of natural language processing (NLP), enabling machines to capture subtle meaning and context in text. By converting sentences into compact, fixed-size vectors, sentence transformers unlock a range of applications, including semantic search (identifying relevant documents or passages based on their meaning and context), sentence similarity assessment (measuring the degree of semantic equivalence between two sentences), and paraphrase detection (identifying sentences that convey the same meaning using different words or phrasing). In this study, we fine-tuned multiple state-of-the-art sentence transformer models on a custom dataset specifically designed for sentence similarity tasks and evaluated their performance through rigorous benchmarking. Our findings yield a surprising insight: the choice of model is highly task-dependent, with larger models not always outperforming smaller ones, which underscores the importance of model selection for optimal performance in specific NLP tasks.