LLMs Misinterpret Figurative Language, Raising Product Risks

The article analyzes how large language models routinely misinterpret non-literal expressions—sarcasm, dark humor, metaphors, idioms, and analogies—highlighting empirical weaknesses such as roughly 50% accuracy on joke-segment detection and 40–60% literal outputs when asked to generate figurative language. It attributes these failures to distributional training and lack of pragmatic intent modeling, and recommends benchmarks, detection pipelines, and design patterns for safer chatbots and content tools.