Why judge models on held-out data?

• Held-out data trains faster than the rest
• Training data is too small to measure
• The point is predicting cases never met before

What is overfitting?

• Fitting training data, including its noise, so new data fails
• Using a model too simple to catch the pattern
• Testing on data the model never saw

A whole field tuning against one shared leaderboard can overfit the benchmark together.

Circle one:   True   False

A new method beats a baseline that its authors barely tuned. What is the honest next step?

• Publish the win as proven progress
• Tune the baseline as hard, then retest on fresh data
• Tune the new method even harder instead

A benchmark predates the pretraining corpus and scores look superb. What should you suspect?

• Contamination: the test may sit inside the training text
• Underfitting: the model is too simple for the test
• Slow hardware: scores need faster chips

Which list states what a benchmark win must assume to mean progress?

• Separate test, equal tuning, fresh-data check
• Bigger model, longer training, louder launch
• Older test, weaker baseline, single metric

A vendor reports one accuracy number with no tuning details and no fresh-data check. What do you reply?

• Accept it, since arithmetic cannot mislead
• Ask for the leak, tuning, and freshness guards before believing the claim
• Ask for a bigger number on the same test

A leaderboard climbs yearly while user complaints hold steady. What is the best diagnosis?

• Users complain whatever the quality
• The field overfits the shared test; the number rises, real skill stalls
• Yearly climbs always equal real progress