LLMs can handle some types of ambiguity in language by using context to infer the most likely interpretation. For example, if given the sentence, “He saw the man with the telescope,” an LLM can provide plausible explanations based on surrounding context or user clarification.
However, they may struggle with deeply ambiguous or abstract scenarios where context is insufficient. For instance, subtle linguistic nuances, cultural references, or idiomatic expressions might lead to misinterpretation. This is because LLMs rely on statistical patterns in training data and lack genuine understanding.
Developers can improve how LLMs manage ambiguity by designing workflows that provide additional context or allow users to refine their queries. While LLMs are effective in many practical scenarios, they might require human oversight or supplemental systems to address highly ambiguous situations.