Task-Specific and Domain-specific Fine-Tuning: An LLM could be fine-tuned for a specific application that performs one single task (task-specific fine-tuning), e.g., summarizing a text, or a domain of related tasks (domain-specific fine-tuning), e.g., reading legal documents to answer legal questions, summarize a court dockets, analyzing a contract, etc.
Two common approaches that are used for question-answering LLMs are dialogue fine-tuning and instruction fine-tuning. Dialogue fine-tuning involves fine-tuning the LLM on a dialogue dataset. This means exposing the LLM to many examples of question-answers pairs. Instruction fine-tuning involves fine-tuning the LLM using natural language instructions. Instead of providing conversations, we provide instructions that explain how the model should behave or respond to achieve a specific goal.  They use a set of labeled data as {prompt, response} pairs to train the pre-trained LLM.  Limit your response to 200 words.
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