Part 6/10:
Data Collection: Accumulating large, annotated conversation datasets.
Prompt Design: Creating prompts that elicit detailed and structured responses.
Training Process: Using machine learning techniques to refine the model based on curated datasets.
A critical point is that fine-tuning data often consists of JSON lines files, containing prompt-response pairs that guide the model toward desired behavior.
Reinforcement Learning with Human Feedback (RLHF)
The conversation features an in-depth discussion on how RLHF can enhance model alignment with human preferences. The process involves:
Human Evaluation: Providing feedback (thumbs up/down) on generated responses.
Reward Models: Training a separate model to predict the quality of outputs.