Large language models (LLMs) are impressive, but they can sometimes generate incorrect or even toxic content. This "hallucination" problem raises serious trustworthiness concerns as LLMs become more integrated into our lives. A new research paper introduces "LeCov," a multi-level testing approach designed to uncover these hidden flaws in LLMs before they cause real-world harm. Think of it like a rigorous inspection process for AI. LeCov dives deep into the inner workings of LLMs, examining the attention mechanism, neuron activations, and the model's own uncertainty about its responses. It then uses this information to identify potential weaknesses and prioritize which tests are most likely to reveal critical errors. In their experiments, the researchers found that LeCov outperforms existing testing methods, achieving a higher success rate in triggering model defects. This is a crucial step towards building more reliable and trustworthy AI systems. The implications are broad, impacting everything from chatbots to AI assistants, as more robust testing becomes essential for ensuring that LLMs behave as expected. While this research focuses on testing, it also paves the way for future improvements. By understanding how and why LLMs make mistakes, researchers can develop more effective training strategies to mitigate these issues from the start. LeCov represents a significant advance in LLM safety, bringing us closer to AI we can truly rely on.