The paper measures calibration of eight released LLMs on the CAT benchmark. GPT-2 XL, the smallest model evaluated at 1.5B parameters, achieves the lowest accuracy (average acc@50 of 0.140 for phrase-level and 0.143 for sentence-level tasks) yet also shows the lowest overconfidence, with average ECE of 0.056 (phrase) and 0.052 (sentence), well below Vicuna-13B's 0.118 and 0.111. The authors explicitly note this trade-off: smaller models are less accurate but better calibrated.
The comparison spans models of very different sizes and architectures (1.5B to 13B), so the calibration difference may reflect architecture as well as scale. The paper does not isolate size as the sole variable.