Imagine having an AI assistant that reads your code and provides concise summaries, saving you hours of effort. That's the promise of large language models (LLMs). But how can we be sure these summaries are reliable? A new research paper, "Enhancing Trust in LLM-Generated Code Summaries with Calibrated Confidence Scores," tackles this challenge head-on. The problem is that LLMs, while impressive, can sometimes produce summaries that are too long, irrelevant, or just plain wrong. This research introduces the idea of 'calibrated confidence scores.' Essentially, it's a way for the LLM to tell us how sure it is about the accuracy of its summary. Instead of blindly trusting the AI, developers can use these scores to decide whether a summary is good enough to use directly, needs further review, or should be discarded. The researchers explored different ways to calculate these confidence scores, finding that simply averaging the LLM's per-token probabilities isn't enough. They found that a technique called 'rescaling,' which adjusts the probabilities to better match the actual accuracy, significantly improves reliability. Interestingly, they also discovered that the LLM's confidence is often higher for later tokens in the summary, while the earlier tokens are actually better indicators of overall quality. This research offers a practical way to make AI-generated code summaries more trustworthy. By providing a measure of confidence, it empowers developers to use LLMs more effectively, ultimately boosting productivity and code understanding. While challenges remain, such as capturing the developer's intent behind the summary, this work represents a significant step towards reliable AI assistance in software development.