NLP models can handle idioms and metaphors to some extent, but their ability depends on the model’s training data, architecture, and the context provided. Modern models like BERT or GPT use large-scale pretraining to learn patterns in language, including figurative expressions. For example, a model might learn that “break the ice” often refers to starting a conversation, not literal ice-breaking. However, this understanding is statistical rather than true comprehension—it’s based on recognizing associations in data, not grasping abstract concepts like humans do. Models often struggle with rare idioms or context-dependent metaphors, especially when the literal meaning conflicts with the figurative use.
A key factor is how idioms are represented in training data. If a phrase like “spill the beans” (meaning to reveal a secret) appears frequently in contexts where “reveal” or “secret” are present, the model may associate it with disclosure. For instance, in the sentence “She spilled the beans about the surprise party,” a model trained on diverse text might infer the correct meaning by linking “spilled” and “surprise party” to contexts involving secrecy. However, if the same idiom appears in an unfamiliar scenario—like "He spilled the beans into the pot"—the model could misinterpret it literally. Similarly, metaphors like “time is a thief” might be processed correctly if the model has seen similar analogies, but ambiguous cases (e.g., “the weight of silence”) may confuse it due to lack of clear contextual cues.
Developers can improve idiom handling by fine-tuning models on domain-specific data or using techniques like contextual embeddings. For example, a model fine-tuned on movie reviews might better recognize that “the movie was a rollercoaster” refers to emotional ups and downs. However, challenges remain. Some metaphors rely on cultural knowledge (e.g., “kick the bucket” for death) that models might miss if not exposed to enough examples. Additionally, models may overfit to common phrases and fail with creative or novel metaphors. While NLP models have made progress in processing figurative language, their performance is still inconsistent, requiring developers to validate outputs carefully and add post-processing rules or external knowledge bases where necessary.