What step is critical when preparing data to be used for fine-tuning ChatGPT?

A critical step when preparing data to be used for fine-tuning ChatGPT is formatting the data into the required input format that the model expects. Different fine-tuning approaches require specific data structures. For example, many fine-tuning tasks require prompt-completion pairs, where each data point consists of an input prompt and the corresponding desired output or completion. For question-answering tasks, the data needs to be formatted as question-answer pairs. If the data is not properly formatted, the model will not be able to learn from it effectively, leading to poor performance. The formatting step involves transforming the raw data into the required structure, ensuring that each data point is correctly labeled and organized. This might include creating specific columns for prompts and completions, adding special tokens to indicate the beginning or end of a sequence, or converting the data into a specific file format (e.g., JSON, CSV) that the fine-tuning algorithm can process. Getting the format correct is a mandatory prerequisite for successfully fine-tuning the model.