Can AI truly understand what we mean, or are they just parrots mimicking human language? This question is central to the ongoing development of large language models (LLMs). A fascinating new study delves into this by exploring how LLMs handle "pragmatic implicature," the art of reading between the lines. Specifically, researchers looked at how models like BERT and GPT-2 interpret the word "some." Logically, "some" means "at least one and possibly all." However, in everyday conversation, we often infer "some" to mean "not all." Think about it: if someone says, "Some students passed the exam," we naturally assume not all students passed. The researchers found that, without context, both BERT and GPT-2 tend to interpret "some" as "not all," much like humans. But when the context was manipulated—for instance, by presenting different leading questions like "Did all students pass?" versus "Did any students pass?"—BERT seemed less sensitive to these changes than GPT-2. GPT-2's performance aligned more closely with human behavior, where inferring meaning requires more mental effort in specific contexts. These findings highlight key differences in how various LLMs process language. While they both seem to grasp basic pragmatic inference, they do so through different mechanisms, with BERT leaning towards a default "not all" interpretation of "some," and GPT-2 being more contextually driven. This research has exciting implications for the future of AI. As LLMs become increasingly sophisticated, understanding how they grapple with the nuances of human language is essential to creating truly conversational AI. The ability to infer meaning beyond literal words is crucial for genuine human-computer interaction, and studies like this pave the way toward that goal.