Addressing Overfitting and Underfitting in LLM Training

In today’s world, language models have taken center stage due to their remarkable ability to comprehend, generate, and manipulate human language. 30% of businesses plan to use unstructured data to improve the accuracy of LLMs (Source). One of the fundamental challenges in training these language models is striking the right balance between complexity and generalization. This challenge is encapsulated in the concepts of overfitting and underfitting, which are critical aspects of model training and can greatly impact the performance of the final model.

Complexity and Generalization in LLM Training

Training a LLM to understand and generate coherent text can be difficult. The goal is to create a model that not only performs well on the training data but also exhibits strong performance on new, unseen data. Achieving this balance between complexity and generalization is akin to a delicate dance. Two other factors play an important role in this balance:

• Bias: Assumptions made by a model to make a function easier to learn. It is actually the error rate of the training data. When the error rate has a high value, we call it High Bias and when the error rate has a low value, we call it low Bias.
• Variance: The difference between the error rate of training data and testing data is called variance. If the difference is high then it’s called high variance and when the difference in errors is low then it’s called low variance. Usually, we want to make a low variance for generalized our model.

What is Overfitting?

Overfitting occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. As a result, the model performs exceptionally well on the training data but struggles when presented with new, unseen data. In essence, the model has learned to "fit" the training data so closely that it fails to generalize its knowledge to different contexts.

Overfitting is like an overly enthusiastic student who memorizes every answer in a textbook without understanding the underlying concepts. In machine learning, this translates to a model that has become excessively specialized in the training data without grasping the broader principles.

Consider a language model tasked with generating movie reviews. As the training progresses, the model might inadvertently start incorporating specific phrases, characters, or plot details from the training data into its generated text. While this might result in highly convincing text that mimics the style of the training data, it often falls apart when faced with unseen movie plots.

Overfitting can be detected by closely monitoring the model's performance on a separate validation dataset that it has not seen during training. If the validation performance begins to degrade while the training performance remains high, it's a telltale sign that the model has started overfitting. In such cases, the model's ability to generalize to new data is compromised.

What are the Main Reasons for Overfitting?

There are several reasons why overfitting can occur:

• High variance and low bias
• The model is too complex
• The size of the training data

How to Tackle Overfitting?

Here is how you can tackle overfitting in LLM training:

• Increase training data
• Reduce model complexity
• Early stopping during the training phase
• Ridge Regularization and Lasso Regularization
• Use dropout for neural networks to tackle overfitting

What is Underfitting?

Underfitting arises when a model is too simplistic to capture the complexities present in the training data. An underfit model lacks the capacity to grasp intricate patterns, leading to poor performance on both the training data and new data. It's as if the model has failed to learn even the basic structure of the language.

Underfitting is similar to sending an unprepared athlete to compete in a high-stakes race. No matter how hard they try, they lack the necessary skills and training to perform well. In machine learning, an underfit model struggles to capture the nuances and intricacies present in the training data.

Continuing with the movie review example, an underfit language model might generate reviews that lack depth, coherence, and meaningful insights. The generated text might appear disjointed, with little connection to the movie's plot or characters. This happens because the model's simplicity prevents it from recognizing and learning the complex relationships between different elements in the data.

Detecting underfitting is relatively straightforward: both training and validation performance remain subpar. The model's inability to capture even basic patterns becomes evident in its overall poor performance on a variety of tasks.

What are the Main Reasons for Underfitting?

There are several reasons why underfitting can occur:

• High bias and low variance
• The model is too simple
• The size of the training dataset used is not enough
• Training data is not cleaned and also contains noise in it

How to Tackle Underfitting?

Here is how you can tackle underfitting in LLM training:

• Increase model complexity
• Increase the number of features, performing feature engineering
• Remove noise from the data
• Increase the number of epochs or increase the duration of training to get better results

The Balance between Overfitting and Underfitting

The journey of training a language model is a dynamic and iterative process. Achieving the optimal balance between overfitting and underfitting requires careful consideration, experimentation, and continuous refinement.

As the model is trained, it's important to monitor its performance on both the training and validation datasets. If the model's performance on the validation data starts to degrade while the training performance remains high, it's a signal that overfitting may be occurring. Adjustments can help rein in the overfitting tendencies.

Ultimately, the goal is to strike the right chord between complexity and generalization. A well-tuned language model is one that can seamlessly generate coherent and contextually relevant text, not only mirroring the training data but also demonstrating a deep understanding of the underlying language structure.

Conclusion

Overfitting and underfitting are not mere roadblocks; they are integral to the learning process of language models. As researchers and practitioners continue to delve into the intricacies of model training, new techniques, and insights will emerge to tackle these challenges. Appy Pie is a pioneering no-code AI platform that harnesses the power of language models to create intelligent applications that enhance various aspects of our lives.

The evolution of language models reflects the broader journey of AI and machine learning—a constant push toward greater understanding, efficiency, and innovation. By unraveling the mysteries of overfitting and underfitting, we pave the way for language models that are not just proficient imitators, but true masters of human language.

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