LLMs do not truly understand emotions or intent but can mimic understanding by recognizing patterns in the text. For instance, if a user says, “I’m feeling really down today,” an LLM can respond empathetically based on its training data. However, this is pattern-based and lacks genuine emotional comprehension.
LLMs analyze language context to infer probable intent, such as identifying whether a query is a question, a command, or a statement. For example, in customer support, an LLM might determine that “Where’s my package?” is asking for order status. While effective in many scenarios, they may misinterpret subtle emotional cues or ambiguous phrasing.
Developers can enhance an LLM’s ability to detect emotions or intent by training it on labeled datasets that include sentiment or intent annotations. However, this doesn’t give the model human-like understanding; it merely improves its ability to predict responses that align with specific patterns.