Sentence Transformers and large language models (LLMs) like GPT both leverage transformer architectures but serve distinct purposes. Sentence Transformers are designed to generate dense vector representations (embeddings) of text, capturing semantic meaning for tasks like similarity comparison or retrieval. Models like GPT, however, are autoregressive, decoder-focused transformers trained for text generation. While both use attention mechanisms, their architectures diverge: Sentence Transformers often use siamese or triplet networks built on encoder-based models (like BERT), whereas GPT uses a decoder-only structure optimized for predicting the next token in a sequence. For example, a Sentence Transformer might map "cat" and "kitten" to nearby vectors, while GPT would generate a continuation like "is a small domesticated animal."
Sentence Transformer models are typically smaller and more specialized than general-purpose LLMs. They are often based on scaled-down versions of encoder architectures (e.g., BERT-base with 110M parameters) and fine-tuned for specific embedding tasks using contrastive or triplet loss. This specialization allows them to excel in focused applications like semantic search or clustering without the computational overhead of larger models. For instance, the all-MiniLM-L6-v2 model has only 22M parameters, making it efficient for real-time embedding generation. In contrast, GPT-3.5 has 175B parameters, requiring significant resources for inference.
The specialization of Sentence Transformers stems from their training process. While LLMs like GPT are pretrained on broad corpora for language modeling, Sentence Transformers are often further trained on labeled datasets (e.g., pairs of similar sentences) to refine their understanding of semantic relationships. This targeted training—combined with architectural optimizations like pooling layers to aggregate token embeddings—makes them more effective for tasks where precise semantic encoding matters. However, they lack the generative flexibility of LLMs, trading broad capability for efficiency and accuracy in their niche.