3.3 Test data/user data
When deploying LLMs for downstream tasks, we often face challenges stemming from distributional differences between
the test/user data and that of the training data. These disparities may encompass domain shifts [132], out-of-distribution
variations [31], or even adversarial examples [82]. Such challenges significantly hinder fine-tuned modes’ effectiveness
in real-world applications. They fit into a specific distribution and have a poor ability to generalize to OOD data.
However, LLMs perform quite well facing such scenarios because they do not have an explicit fitting process. Moreover,
recent advancements have further enhanced the ability of language models in this regard. The Reinforcement Learning
from Human Feedback (RLHF) method has notably enhanced LLMs’ generalization capabilities [77]. For example,
InstructGPT demonstrates proficiency in following various instructions for a wide range of tasks and occasionally
Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond