How do NLP models deal with slang or informal language?

NLP models handle slang and informal language primarily through training on diverse datasets, subword tokenization, and contextual understanding. When models are trained on data that includes informal text—like social media posts, forums, or chat logs—they learn patterns in slang usage. For example, a model trained on Twitter data might recognize that “lit” often means “exciting” in informal contexts. Tokenization methods like Byte-Pair Encoding (BPE) or WordPiece break unknown slang into subwords (e.g., splitting “finna” into “finn” + “a”), allowing models to process terms not in their vocabulary. This helps models handle variations like “bruh” or “af” (as in “cool af”) even if they weren’t explicitly seen during training.

Contextual embeddings, such as those from transformer models like BERT or GPT, play a key role. These models analyze surrounding words to infer slang meanings. For instance, in “That concert was fire,” the word “fire” is mapped to a positive meaning based on the context of “concert” and the sentence structure. Attention mechanisms allow the model to weigh relationships between words, distinguishing between “sick” meaning “ill” in a medical context versus “awesome” in a casual conversation. Fine-tuning further adapts models to specific domains—a customer support chatbot might be retrained on chat logs containing abbreviations like “FYI” or slang like “ghosting” to improve performance.

Challenges remain due to slang’s evolving and regional nature. A model trained on older data might miss newer terms like “rizz” (charisma) or “cheugy” (out of touch). Solutions include continuous retraining with fresh data and using user feedback to flag unrecognized terms. Some systems employ slang dictionaries or rule-based preprocessing to map informal phrases to formal equivalents (e.g., replacing “u” with “you”). However, over-reliance on static rules can fail as slang changes. Developers often combine these approaches—using robust tokenization, context-aware models, and periodic updates—to balance flexibility and accuracy when handling informal language.