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As large language models attract increasing attention and find widespreadapplication, concurrent challenges of reliability also arise at the same time.Confidence calibration, an effective analysis method for gauging thereliability of deep models, serves as a crucial tool for assessing andimproving their reliability. However, such investigation has been comparativelyunderexplored. In this work, we conduct a systematic examination of thecalibration of aligned language models throughout the entire constructionprocess, including pretraining and alignment training. At each stage, weinvestigate how different training settings, such as parameter scales andtraining data, affect model calibration. To thoroughly assess modelcalibration, we evaluate models on three most concerned aspects: generation,factuality and understanding. Our work sheds light on whether popular LLMs arewell-calibrated and how the training process influences model calibration.

On the Calibration of Large Language Models and Alignment