Model Evaluation (Evaluation)

Understanding Its Purpose First

Model evaluation examines results using clear tasks and standards, determining whether capabilities meet the intended goals.

Understanding Through an Example

Lesson planning evaluation might ask: whether the total duration adds up to 40 minutes, whether students have learned prerequisite knowledge, whether citations correspond to the original text, and whether classroom activities are feasible. Focusing only on writing fluency can easily miss these issues.

Going a Step Deeper

Training loss measures how well it fits the training objective; evaluation measures whether it can accomplish real-world tasks. Test questions should be as independent as possible from training and debugging samples; evaluation can include programmatic checks, human review, rating scales, and observation of actual use. Different metrics reflect different facets.

What It Cannot Tell You

High scores are only meaningful within the scope of that particular evaluation. One impressive example doesn't represent stable capability; automated evaluation can also miss errors. Visual outputs like PPTs require checking actual rendering effects; text-only inspection is insufficient.

Next Steps

• Training Data
• Post-training
• Hallucination

References

• PyTorch · Optimizing Model Parameters: batches, learning rate, loss, gradients, SGD updates, and test set evaluation.
• OpenAI · Optimizing LLM accuracy: context, task instructions, and evaluation affect output accuracy; one-shot generation does not guarantee correctness.

Verified on 2026-09-09; original papers are used to explain mechanisms; examples in the text are for instructional purposes only.