Robustness and Calibration. The accuracy and robustness of the LLMs are shown to have a very strong correlation [59].
The models that have high accuracy on the scenario also have good robustness. However, the robustness of the zero-shot
becomes worse after being tuned on extra application-specific tasks data [116]. This may due to overfitting, which
leads to poor generalizability due to the extremely high complexity of the model and the limited training samples
from downstream tasks [43]. In a similar vein, it has been observed that fine-tuning a model can result in significant
miscalibrations, owing to over-parameterization [51]. Therefore, fine-tuned models may not be an optimal choice when
robustness and calibration are critical considerations. However, human alignment has been found as a potential solution
for enhancing model robustness. InstructGPT davinci v2 (175B*) has been shown to outperform other models in terms

8https://github.com/tloen/alpaca-lora
Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond