Large language models (LLMs) can perform an impressive array of tasks, from writing code to summarizing medical notes. But how do we truly evaluate their capabilities across such diverse applications? A new research paper explores how people perceive and generalize LLM abilities, revealing a surprising disconnect between actual performance and human expectations. The study investigates the "human generalization function," which describes how we extrapolate an LLM's skills based on limited interactions. For instance, if an LLM aces a college physics question, we might assume it can handle basic math, but not necessarily Japanese literature. The researchers collected nearly 19,000 examples of human generalizations across 79 tasks, using established benchmarks like MMLU and BIG-Bench. They found that people generalize in consistent, predictable ways, and this process can be modeled using natural language processing techniques. Surprisingly, simpler models like BERT were better at predicting human generalizations than larger, more complex LLMs. This suggests that the nuances of human reasoning might get lost as models scale up. This has significant implications for real-world LLM deployment. The study reveals a potential pitfall: larger models can inspire undue confidence. While generally capable of answering more questions, their limitations might be overlooked, leading to inappropriate deployment and subpar performance. In high-stakes situations, this misalignment between perceived and actual competence can be especially problematic. For example, an LLM might correctly answer a complex economics question but fail at basic arithmetic – a discrepancy that could easily mislead a user relying on the model for financial analysis. This research highlights the crucial need for aligning LLM capabilities with human intuition. Future research should focus on improving these models, refining the human-machine interface, and understanding how people make decisions about deploying AI systems. The ultimate goal is to ensure that LLMs are used effectively and responsibly, maximizing their benefits while mitigating potential risks.