Why fine-tuning adapts models to domains in Prompt Engineering / GenAI - Why Metrics Matter

Metrics & Evaluation - Why fine-tuning adapts models to domains

Which metric matters for this concept and WHY

When fine-tuning a model to a specific domain, the key metrics to watch are accuracy, precision, and recall. These show how well the model adapts to the new domain data. Fine-tuning aims to improve these metrics on domain-specific tasks compared to the original model.

Confusion matrix or equivalent visualization

    Confusion Matrix Example (Domain-Specific Task):

          Predicted
          Pos   Neg
    Actual Pos  85    15
           Neg  10    90

    Total samples = 200
    TP=85, FP=10, TN=90, FN=15
    

Precision vs Recall tradeoff with concrete examples

Fine-tuning helps balance precision and recall for the domain. For example, in medical diagnosis, recall is critical to catch all cases, so fine-tuning focuses on reducing missed positives. In spam detection, precision is key to avoid marking good emails as spam, so fine-tuning improves precision.

What "good" vs "bad" metric values look like for this use case

Good: After fine-tuning, precision and recall both improve significantly on domain data, e.g., precision > 0.85 and recall > 0.80.

Bad: Metrics stay low or only one improves, e.g., precision 0.50 and recall 0.30, showing poor adaptation.

Metrics pitfalls

• Overfitting: Fine-tuning too much can make the model only work well on training domain data but fail on new examples.
• Data leakage: Using test data during fine-tuning inflates metrics falsely.
• Ignoring recall or precision: Focusing on one metric can hurt overall usefulness.

Self-check question

Your fine-tuned model has 98% accuracy but only 12% recall on domain positives. Is it good for production? Why or why not?

Key Result

Fine-tuning improves domain-specific precision and recall, key to adapting models effectively.

Practice

1. Why do we fine-tune a pre-trained model for a specific domain?

easy

A. To make the model larger and more complex

B. To reduce the model's accuracy on general tasks

C. To erase all previous knowledge from the model

D. To help the model learn details specific to that domain

Solution

1. Step 1: Understand the purpose of fine-tuning

   Fine-tuning adjusts a general model to perform better on a specific topic or style by teaching it new details.

2. Step 2: Identify the effect on the model

   Fine-tuning helps the model learn domain-specific details without losing all previous knowledge.

3. Final Answer:

   To help the model learn details specific to that domain -> Option D

4. Quick Check:

   Fine-tuning = domain adaptation [OK]

Hint: Fine-tuning adds domain details, not erases knowledge [OK]

Common Mistakes:

• Thinking fine-tuning makes the model forget everything
• Believing fine-tuning always makes the model bigger
• Assuming fine-tuning reduces accuracy on all tasks

2. Which of the following is the correct way to start fine-tuning a model in Python using a library?

easy

A. model.fine_tune(data, epochs=3)

B. model.train(data, epochs=3)

C. model.fit(data, epochs=3)

D. model.tune(data, epochs=3)

Solution

1. Step 1: Recognize common fine-tuning method names

   In many ML libraries, fit is used to train or fine-tune models on new data.

2. Step 2: Compare options to common usage

   fine_tune and tune are not standard method names; train is less common than fit for fine-tuning.

3. Final Answer:

   model.fit(data, epochs=3) -> Option C

4. Quick Check:

   Fine-tuning uses fit() method [OK]

Hint: Use fit() to train or fine-tune models in Python [OK]

Common Mistakes:

• Choosing non-existent method names like fine_tune()
• Confusing train() with fit() in common libraries
• Assuming tune() is a valid method

3. Given this code snippet for fine-tuning a model, what will be the output loss after training?

initial_loss = 0.8
for epoch in range(3):
    initial_loss *= 0.7
print(round(initial_loss, 2))

medium

A. 0.27

B. 0.41

C. 0.56

D. 0.34

Solution

1. Step 1: Calculate loss after each epoch

   Start with 0.8, multiply by 0.7 three times: 0.8 * 0.7 = 0.56, 0.56 * 0.7 = 0.392, 0.392 * 0.7 = 0.2744.

2. Step 2: Round the final loss

   Rounded to two decimals: 0.27.

3. Final Answer:

   0.27 -> Option A

4. Quick Check:

   Loss after 3 epochs = 0.27 [OK]

Hint: Multiply loss by decay each epoch, then round [OK]

Common Mistakes:

• Multiplying fewer times than epochs
• Rounding before final multiplication
• Choosing wrong rounded value

4. You tried fine-tuning a model but the accuracy did not improve. Which of these is the most likely error in your code?

model = load_pretrained_model()
model.fit(new_data)
model.evaluate(test_data)

medium

A. Not specifying epochs in fit() so training was too short

B. Using evaluate() before fit()

C. Loading the wrong model type

D. Not normalizing the test data

Solution

1. Step 1: Check the fit() method usage

   Without specifying epochs, fit() may run only one epoch or default minimal training, insufficient for fine-tuning.

2. Step 2: Understand impact on accuracy

   Too few training steps means the model doesn't learn new domain details, so accuracy stays low.

3. Final Answer:

   Not specifying epochs in fit() so training was too short -> Option A

4. Quick Check:

   Short training = no accuracy gain [OK]

Hint: Always set epochs to train enough during fine-tuning [OK]

Common Mistakes:

• Assuming evaluate() order matters before fit()
• Ignoring data normalization effects
• Not checking model type mismatch

5. You have a general language model and want it to perform well on medical text. Which fine-tuning approach best adapts it to this domain?

hard

A. Train the model from scratch only on medical data

B. Fine-tune the pre-trained model with a small medical dataset using low learning rate

C. Use the pre-trained model without any changes

D. Fine-tune the model with random unrelated data to increase size

Solution

1. Step 1: Compare training from scratch vs fine-tuning

   Training from scratch needs lots of data and time; fine-tuning uses existing knowledge and adapts efficiently.

2. Step 2: Identify best fine-tuning practice

   Using a small medical dataset with a low learning rate helps the model learn domain details without forgetting general knowledge.

3. Final Answer:

   Fine-tune the pre-trained model with a small medical dataset using low learning rate -> Option B

4. Quick Check:

   Fine-tune + small data + low rate = best domain fit [OK]

Hint: Fine-tune with small domain data and low learning rate [OK]

Common Mistakes:

• Training from scratch without enough data
• Using unrelated data for fine-tuning
• Skipping fine-tuning and using general model only