4.6 Real world "tasks"
In the last part of this section, we would like to discuss the usage of LLMs and fine-tuned models in real-world "tasks". We
use the term "tasks" loosely, as real-world scenarios often lack well-formatted definitions like those found in academia.
Many requests to models even cannot be treated as NLP tasks. Models face challenges in the real world from three
perspectives:

• Noisy/Unstructured input. Real-world input comes from real-world non-experts. They have little knowledge
about how to interact with the model or even cannot use texts fluently. As a result, real-world input data can be
messy, containing typos, colloquialisms, and mixed languages, unlike those well-formed data used for pre-training
or fine-tuning.
Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond