Fine-Tune a Language Model on Question-Answer Pairs

# Fine-Tune a Language Model on Question-Answer Pairs Explain how to fine-tune a pretrained language model using question-and-answer pairs, from data construction and token masking through optimization, evaluation, and deployment safeguards. ### Constraints & Assumptions - Training and evaluation pairs must be separated without near-duplicate leakage. - The intended model may be decoder-only or encoder-decoder; state which format is used. - User or proprietary data requires an explicit privacy and licensing boundary. ### Clarifying Questions to Ask - Should loss apply to the question tokens, answer tokens, or both? - What behavior is the fine-tune intended to change? - Which baseline and held-out tasks must be preserved? ```hint Align labels with behavior Show exactly which tokens produce training loss and how inference formatting matches the training template. ``` ### What a Strong Answer Covers - Data cleaning, formatting, splitting, tokenization, and masking. - Optimization choices, sequence length, batching, precision, and checkpointing. - Task, quality, safety, and capability-retention evaluation. - Versioned deployment, monitoring, rollback, and data governance. ### Follow-up Questions 1. How would you handle answers with several valid phrasings? 2. When would retrieval or prompting be preferable to fine-tuning?