Sentence Transformers, while powerful for generating sentence embeddings, face several challenges in understanding and representing sentence meaning. These limitations stem from their architecture, training data, and inherent biases in language modeling. Developers should be aware of these constraints when applying these models to real-world tasks.
One key limitation is their handling of context and ambiguity. Sentence Transformers typically process each sentence in isolation, which can lead to issues when meaning depends on broader context. For example, the sentence “It’s cold” could refer to weather, a drink, or a person’s demeanor, but the model might generate the same embedding regardless of context. This becomes problematic in dialogue systems where previous utterances provide crucial context. Additionally, models struggle with coreference resolution (linking pronouns to their antecedents) and long-range dependencies in multi-sentence texts. A sentence like “The doctor called the nurse because she was late” becomes ambiguous in isolation – the model can’t reliably determine whether “she” refers to the doctor or nurse without external context.
Another challenge is domain specificity and rare linguistic patterns. These models are typically trained on general web text, which can limit their effectiveness in specialized domains. For example, in legal documents where phrases like “party of the first part” have specific meanings, the model might not capture the nuanced distinctions compared to everyday usage. Technical jargon, regional dialects, and newly emerging slang (like internet memes or crypto terminology) often aren’t represented well. Even when using fine-tuning, the model’s performance remains constrained by its original training data distribution. A medical search engine using Sentence Transformers might struggle with queries containing terms like “discharge” (which could mean patient release or bodily fluid depending on context) without explicit domain adaptation.
Finally, there’s an inherent limitation in capturing true semantic understanding. The models excel at surface-level similarity but can miss logical relationships and deeper meaning. For instance, they might rate “Cats chase mice” and “Mice are chased by cats” as highly similar (good), but also potentially give high similarity to “Cats eat mice” despite the different action (bad). They struggle with negation (“The movie wasn’t boring” vs “The movie was exciting”) and subtle emotional tones conveyed through sarcasm or irony. A user review stating “Sure, the service was fast – if you consider 3 hours fast!” would likely be misrepresented as positive by the embedding. These limitations suggest that while Sentence Transformers are excellent for many similarity tasks, they shouldn’t be treated as true semantic understanding systems without additional validation layers.