Why might using the [CLS] token embedding directly yield worse results than using a pooling strategy in Sentence Transformers?

Using the [CLS] token embedding directly often yields worse results than pooling strategies in Sentence Transformers because the [CLS] token’s pretraining objective and inherent limitations make it less suitable for general semantic tasks without further optimization. While the [CLS] token in models like BERT is designed to capture sentence-level information for classification tasks, its effectiveness depends heavily on the specific training objectives. For example, BERT’s pretraining includes a next-sentence prediction (NSP) task, which trains the [CLS] token to distinguish whether two sentences are related. However, this narrow focus can make the [CLS] embedding less effective for tasks like semantic similarity or retrieval, where capturing nuanced relationships between sentences is critical. In contrast, pooling strategies aggregate information from all token embeddings, which often provides a more robust representation of the full sentence context.

Pooling strategies, such as mean or max pooling, mitigate the limitations of relying on a single token by combining information from the entire sequence. For instance, mean pooling averages all token embeddings in the sequence, effectively distributing the semantic weight across the sentence rather than relying on one potentially noisy or biased token. This is especially useful when sentences vary in length or structure, as pooling ensures that all tokens contribute to the final representation. For example, in a sentence like “The quick brown fox jumps over the lazy dog,” the [CLS] token might focus on specific keywords (e.g., “jumps” or “lazy”), while mean pooling captures relationships between all words, preserving nuances like the action (jumping) and the subject (fox and dog). This aggregation reduces the risk of overemphasizing irrelevant tokens and creates a more balanced embedding.

Another key factor is the fine-tuning process. Sentence Transformers often use contrastive or triplet loss objectives during training, which explicitly optimize pooled embeddings to align similar sentences and separate dissimilar ones. For example, when trained on Natural Language Inference (NLI) datasets, the model learns to distinguish entailment, contradiction, and neutrality by refining the pooled embeddings. The [CLS] token, however, is not directly optimized for these tasks unless the model is retrained with a specific objective targeting it. Without such fine-tuning, the [CLS] embedding remains tied to its original pretraining goals (like NSP), which may not align with downstream tasks. Pooling strategies, combined with task-specific training, thus create embeddings that are both more flexible and better suited to real-world applications like semantic search or clustering.