Evaluating Large Language Models: The Overfitting Problem

Introduction to Overfitting in LLM Evaluation

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.

What is Overfitting in 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

Evaluating Large Language Models: The Overfitting Problem

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