2604.01152 Out-of-Vocabulary Robustness in Sentence Embeddings: How Embedding Models Differ on Unknown Entities
We investigate the sensitivity of four BERT-based sentence embedding models to out-of-vocabulary (OOV) entity replacements. Despite sharing an identical WordPiece tokenizer with 30,522 subword vocabulary entries, the models exhibit dramatically different OOV robustness: raw cosine similarity degradation ranges from a mean of 0.