Handling such real-world scenarios requires coping with ambiguity, understanding context, and handling noisy
input. Compared to fine-tuned models, LLMs are better equipped for this because they have been trained on diverse
data sets that encompass various writing styles, languages, and domains. Additionally, LLMs demonstrate a strong
ability to generate open-domain responses, making them well-suited for these scenarios. Fine-tuned models, on the
other hand, are often tailored to specific, well-defined tasks and may struggle to adapt to new or unexpected user
requests. They heavily rely on clear objectives and well-formed training data that specify the types of instructions
the models should learn to follow. Fine-tuned models may struggle with noisy input due to their narrower focus on
specific distributions and structured data. An additional system is often required as an assistant for fine-tuned models
Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond