Large language models (LLMs) handle idioms and metaphors by relying on patterns learned during training. These models analyze the context surrounding phrases to infer whether a non-literal meaning is intended. For example, when encountering an idiom like “kick the bucket,” the model uses statistical associations from its training data—such as co-occurring words like “died” or "passed away"—to determine that the phrase refers to death rather than a literal action. Similarly, metaphors like “time is a thief” are interpreted by linking “thief” to concepts like loss or stealth in the context of time. This process is not based on explicit rules but on the model’s ability to recognize how words and phrases are commonly used together in similar contexts.
To achieve this, LLMs break down input text into tokens and use attention mechanisms to weigh relationships between words. For instance, in the sentence “She spilled the beans about the surprise party,” the model might focus on “spilled” and “beans” in relation to “surprise party.” By comparing this to patterns in training data (e.g., “spilled the beans” often appears near “secret” or “revealed”), the model infers the idiomatic meaning. For metaphors, such as “the world is a stage,” the model maps “stage” to concepts like performance, roles, or visibility based on how these terms are used in other contexts. This contextual mapping allows the model to generate or interpret text that aligns with the intended figurative meaning, even without explicitly understanding abstract concepts.
However, LLMs can struggle with rare or ambiguous idioms and metaphors. For example, a less common idiom like “chew the fat” (to chat casually) might be misinterpreted if the context is unclear. Similarly, a metaphor like “his heart was a furnace” could be misread as literal if the surrounding text doesn’t provide enough clues. Developers can improve accuracy by fine-tuning models on domain-specific data or using techniques like prompt engineering (e.g., adding “Explain this metaphor:” before the input). While LLMs handle many figurative expressions effectively, their performance ultimately depends on the diversity and quality of their training data, as well as the clarity of the input context provided by the user.