Editing Fine-tuning Large Language Models (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert practitioners treat fine-tuning evaluation as multi-dimensional:

'''Task-specific metrics''': Whatever the downstream task demands — ROUGE for summarization, exact match for QA, pass@k for code generation, human preference rates for chat.

'''General capability retention''': Run the fine-tuned model on standard benchmarks (MMLU, HellaSwag, HumanEval) to verify general capabilities weren't degraded. A model fine-tuned for customer service shouldn't lose its ability to reason.

'''Alignment and safety evaluation''': Does fine-tuning introduce new failure modes? Run adversarial prompts, jailbreak attempts, and harmful content evaluations on the fine-tuned model.

'''Human preference evaluation (A/B testing)''': For conversational models, human raters compare base model vs. fine-tuned model outputs on real user queries. This is the ground truth for whether fine-tuning achieved its goal.

Expert practitioners maintain a '''regression test suite''' — a fixed set of prompts with expected behaviors — and run it after every fine-tuning run to catch regressions automatically.
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