Generalizing vs Overfitting in Language Models

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When it comes to training language models, there’s a fine line between learning and memorizing.

• A model that generalizes well can handle new sentences, new domains, and new speakers.

• A model that overfits may perform brilliantly on training data—but falls apart in the real world.

In this post, we’ll explore the critical tension between generalization and overfitting in n-gram models, show how it plays out using samples from Shakespeare vs the Wall Street Journal, and discuss how issues like dialect, register, and genre impact performance.

1. What Is Overfitting in Language Models?

Overfitting happens when a model becomes so tailored to the training data that it loses the ability to adapt to new, unseen text.

In n-gram models, overfitting appears when:

• The model has memorized specific n-gram sequences

• It fails to predict reasonable alternatives not seen in training

• It assigns zero probability to unseen—but valid—phrases

This is especially common in higher-order n-gram models, like 4-grams or 5-grams.

2. A Simple Example: 4-Gram Memorization

Suppose we train a 4-gram model on the sentence:

“The quick brown fox jumps”

When asked to predict the next word after “The quick brown fox”, the model confidently says “jumps”. But it may assign zero probability to:

• “runs”

• “sleeps”

• “walks”

Why? Because it has never seen those variations. It memorized the one phrase it saw, and now assumes that’s the only possible continuation.

This is overfitting.

3. Sampling Comparison: Shakespeare vs WSJ

Let’s train two 4-gram models:

• One on Shakespearean plays

• One on Wall Street Journal (WSJ) articles

Now let’s generate some sample sentences.

Shakespeare 4-gram:

“Thou art not fit to live. I must be cruel only to be kind.”
“The lady doth protest too much, methinks.”

These sentences are:

• Perfectly fluent

• Often word-for-word matches from Shakespeare

• Rich in emotion and style

But are they truly learned?

Not really—they’re memorized from a small, fixed corpus (~884,000 words total).

WSJ 4-gram:

“The Federal Reserve Board said interest rates will remain steady.”
“The stock market closed higher amid investor optimism.”

Again:

• Fluent.

• Factual-sounding.

• Pulled from repeated financial reporting structures.

But if we tried to use the Shakespeare model to generate business news, or the WSJ model to write drama—it wouldn’t work.

Each model overfits to its genre.

4. Why Overfitting Happens More in Higher-Order N-Grams

The formula for total possible n-grams grows exponentially with vocabulary size $V$:

$$\text{Possible n-grams} = V^n$$

If $V = 10,000$, then:

• Bigrams = 100 million

• Trigrams = 1 trillion

• 4-grams = 10 quadrillion

Most of these n-grams never occur in even massive corpora. So when training a 4-gram model:

• Most predictions are based on rare or single-occurrence phrases

• The model becomes data-hungry, relying on memorized chunks

Unless the training data is extremely large and diverse, high-order n-gram models are likely to overfit.

5. The Cost of Overfitting: Poor Generalization

Let’s say you train a chatbot on formal customer emails. Now you test it on:

• Casual tweets

• Spoken text messages

• Technical documentation

Chances are, it will:

• Misinterpret slang

• Struggle with unseen syntactic patterns

• Produce awkward or irrelevant responses

That’s poor generalization—your model hasn’t learned the underlying structure of language, only the surface patterns of one type of text.

6. Mismatches in Dialect, Register, and Genre

Overfitting also occurs when there’s a mismatch between training and application domains.

Aspect	Example
Dialect	African American English vs Standard English
Register	Casual speech vs formal writing
Genre	News articles vs fictional dialogue

Example: Dialect mismatch

You train on:

“I am going to the store.”

Then encounter:

“I’m finna hit the store.”

An overfit model would fail here. It doesn’t know “finna” means “about to”.

Example: Genre mismatch

A model trained on WSJ headlines might stumble when analyzing Reddit posts, YouTube comments, or TikTok transcripts.

The vocabulary, structure, and social context all shift.

7. Strategies to Encourage Generalization

Here’s how to make your n-gram models less brittle:

✅ Use lower-order n-grams (bigrams/trigrams)
→ More general, less prone to memorization.

✅ Apply smoothing techniques (Laplace, interpolation, backoff)
→ Assign small probabilities to unseen n-grams.

✅ Prune rare n-grams
→ Remove overly specific sequences that aren’t helpful.

✅ Train on diverse corpora
→ Include multiple genres, dialects, and registers.

✅ Switch to subword or neural models
→ More robust to rare words and unseen forms.

8. Key Differences: Generalization vs Overfitting

	Generalization	Overfitting
Learns patterns	✅ Yes	❌ No—memorizes exact phrases
Handles new data	✅ Performs reasonably	❌ Fails on unseen domains
Uses context flexibly	✅ Predicts alternatives	❌ Predicts only what was seen
Model size needed	🔁 Moderate (lower n)	🚀 Large (higher n, more memory needed)
Useful for	Chatbots, summarization, translation	Text replication, mimicry (e.g., fan fiction)

Conclusion

Overfitting is like cramming for a test—you might ace the exam, but you haven’t really learned the material. Similarly, an overfit language model may seem brilliant in the lab, but stumbles in the wild.

The goal is balance: train a model that learns the essence of language—not just the echoes of your training set.

By sampling output, comparing across domains, and designing your model for generalization, you’ll build a system that not only speaks—but understands.

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