How does fine-tuning a ChatGPT model on a specific domain alter its knowledge source prioritization?

Fine-tuning a ChatGPT model on a specific domain alters its knowledge source prioritization by shifting the model's focus towards information that is most relevant to that domain. Fine-tuning is the process of taking a pre-trained language model and further training it on a smaller, domain-specific dataset. Before fine-tuning, the model has a general understanding of language and a broad base of knowledge learned from its initial training on a massive dataset. This initial knowledge base includes a wide range of topics, but it might not be highly specialized in any particular area. After fine-tuning, the model's parameters are adjusted to better reflect the patterns and relationships present in the domain-specific dataset. This means that when generating responses, the model will prioritize information learned from the fine-tuning data over its general knowledge. For example, if a ChatGPT model is fine-tuned on a dataset of medical literature, it will prioritize medical sources and terminology when answering medical questions, even if it has conflicting information from its pre-training data. The model learns to give greater weight to information from the domain-specific data, making it more accurate and reliable within that domain. This prioritization affects how the model selects and synthesizes information when responding to prompts, leading to more focused and domain-relevant answers. It also means the model may be less reliable when asked about topics outside of its fine-tuned domain.