# Evaluating Large Language Models: The Overfitting Problem

> Source: <https://dev.to/tanishq_soni_b115c9b8f874/evaluating-large-language-models-the-overfitting-problem-43f8>
> Published: 2026-06-28 14:45:56+00:00

We've all been there: you train a model, it performs exceptionally well on your test set, but when you deploy it to real-world scenarios, the results are disappointing. This discrepancy often stems from overfitting, a pervasive issue in machine learning that affects even the most advanced large language models (LLMs). At narrivo, we've encountered this problem firsthand, and we believe it's essential to address it in the context of Retrieval-Augmented Generation (RAG) evaluation.

Overfitting occurs when a model becomes too specialized to the training data, capturing noise and outliers rather than the underlying patterns. In RAG evaluation, this means that the model may memorize specific examples from the training set rather than learning to generalize. As a result, when faced with unseen data, the model's performance degrades significantly.

The consequences of overfitting can be severe. A model that has overfit to the training data may:

Consider a language model trained on a dataset of product reviews. During training, the model may learn to recognize specific phrases or patterns that are highly correlated with positive or negative reviews. However, if the model overfits to these patterns, it may fail to generalize to new, unseen reviews that contain different language or tone. For example:

``` python
import torch
from transformers import AutoModelForSequenceClassification

# Load pre-trained model and dataset
model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
dataset = ...

# Train the model
model.train()
for batch in dataset:
    input_ids = batch['input_ids']
    attention_mask = batch['attention_mask']
    labels = batch['labels']
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
    outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
    loss = outputs.loss
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# Evaluate the model on unseen data
model.eval()
unseen_data = ...
with torch.no_grad():
    inputs = unseen_data['input_ids']
    attention_mask = unseen_data['attention_mask']
    outputs = model(inputs, attention_mask=attention_mask)
    predictions = torch.argmax(outputs.logits, dim=1)
```

In this example, if the model has overfit to the training data, it may perform poorly on the unseen data, even if the unseen data is similar in terms of topic or style.

So, how can you mitigate overfitting in RAG evaluation? At narrivo, we recommend the following strategies:

Overfitting is a pervasive problem in machine learning, and it's essential to address it in the context of RAG evaluation. By understanding the causes and consequences of overfitting, you can take steps to mitigate it and develop more robust, generalizable models. As you work on your own LLM projects, we encourage you to ask yourself: what strategies can you use to prevent overfitting and ensure that your model generalizes well to real-world scenarios?