Training vs Test: How We Evaluate Language Models Properly

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A language model is only as good as how we measure it.

In the world of machine learning—and especially in language modeling—it’s easy to fool yourself into thinking your model is amazing. It predicts words perfectly. The probabilities are sky high. You're getting incredible accuracy!

But there's a catch.

If your model has already seen the data it’s being tested on, its performance is artificially inflated. This is called data contamination, and it’s one of the most common traps in language modeling.

In this blog post, we’ll explore how to evaluate language models properly by using training, development, and test sets—and why separating them is critical for building reliable NLP systems.

1. The Three Pillars of Model Evaluation

In any language modeling pipeline, you should divide your data into three parts:

Training Set

• Used to learn the model parameters (e.g., n-gram counts).

• The model “sees” this data and builds statistical estimates from it.

• Example: For an n-gram model, this is where we calculate bigram/trigram frequencies.

Development Set (Dev Set)

• Used to tune hyperparameters, smoothing methods, interpolation weights, etc.

• Like a “sandbox” to try out different configurations.

• Model doesn’t learn from this set, but evaluates performance during development.

• Also called a validation set.

Test Set

• Used only once, at the very end, to evaluate final model performance.

• Gives an unbiased estimate of how the model will behave on new, unseen data.

• Crucial for scientific experiments, benchmarking, and real-world deployment.

2. Why Separation Is So Important: The Contamination Problem

Suppose you're building a speech recognition system. You train your n-gram model on a massive corpus of spoken transcripts. You compute perplexity on some test sentences, and the results are amazing.

But wait…

If those test sentences were also in the training set, your model has already memorized them. The result is meaningless. You haven’t tested your model’s generalization, only its memory.

This is known as data contamination or training on the test set.

What happens when contamination occurs:

• Your model performs well on paper, but fails in production.

• Perplexity is falsely low (inflated accuracy).

• You overestimate the quality of your model.

Just like giving students the answer key before an exam, it defeats the purpose of evaluation.

3. A Real Use Case: Speech Recognition

Imagine you're training a speech recognizer for English lectures on chemistry.

Scenario A (Contaminated):

• Training set: Chemistry lectures from 2022

• Test set: More chemistry lectures from the same 2022 data

Result: The model performs well, but mainly because it’s seen the same phrases and vocabulary.

Scenario B (Proper Evaluation):

• Training set: Chemistry lectures from 2022

• Dev set: Chemistry lectures from early 2023

• Test set: Chemistry lectures from different speakers and new topics

Result: You get a realistic measure of how the model handles variation, new speakers, and unseen vocabulary.

Which result would you trust when deploying the system to transcribe real-time university lectures?

Definitely Scenario B.

4. Overfitting vs Generalization

Another reason to separate the test set: to detect overfitting.

• Overfitting happens when a model learns patterns that are too specific to the training data.

• It performs well on known examples but poorly on new ones.

By keeping the test set separate, you can detect when your model is memorizing, not learning.

5. What About Reusing the Test Set?

You should never repeatedly test on the same test set.

Why?

Because the more you test and tweak based on test performance, the more you implicitly tune to it. Even without intending to, you begin to overfit your model to the test data.

That’s why we have a dev set: to allow experimentation without touching the test set.

Use the test set once—at the end of your development process—like a final exam.

6. How to Split Your Data

There’s no universal rule, but a typical split might look like:

Set	Percentage	Purpose
Training	80%	Learn the model parameters
Development	10%	Tune hyperparameters, smoothing, etc.
Test	10%	Final evaluation

For very large corpora (e.g., web-scale datasets), even a 1% test set can be statistically powerful.

7. A Tip for Language Modelers: Match Genre to Goal

The quality of evaluation also depends on choosing the right kind of test set.

Want to build a customer support chatbot?

→ Use customer chat logs for training and testing.

Training a lecture transcript model?

→ Use academic transcripts or lecture notes—not movie subtitles.

Building a Twitter sentiment analyzer?

→ Use tweets—not formal newswire text.

Align your training, dev, and test sets with your target domain.

8. Summary: Good Practices for Evaluation

Principle	Why It Matters
Use separate training/dev/test sets	Avoid data contamination
Tune only on dev set	Prevent overfitting to the test set
Test only once	Get an unbiased estimate of performance
Match test set to real-world use	Ensure relevance of results
Avoid peeking at test results	Maintain scientific integrity

Conclusion

Evaluating a language model is more than running a script and reading a number. It requires discipline, separation of data, and a clear understanding of what’s being measured.

The true test of a model isn’t how well it performs on data it’s seen—but how it handles the unknown. That’s why evaluation is both a science and an art.

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