Enhancing Fine-Tuning of GPT-3.5-Turbo with AviationQA: A Refined Approach

Frank Morales Aguilera, BEng, MEng, SMIEEE

Boeing Associate Technical Fellow /Engineer /Scientist /Inventor /Cloud Solution Architect /Software Developer /@ Boeing Global Services

Introduction

In my previous article, Fine-Tuning the GPT-3.5-Turbo-0125 Model with AviationQA Dataset, I explored the fine-tuning of the GPT-3.5-Turbo-0125 model using the AviationQA dataset. This process yielded a model with a deeper understanding of aviation-specific terminology and knowledge. However, I’ve since refined this approach by incorporating a validation dataset during the fine-tuning process using OpenAI’s client.fine_tuning.jobs.create API.

The Significance of Validation in Fine-Tuning

Incorporating a validation dataset during fine-tuning is crucial for several reasons:

1. Performance Assessment: The validation dataset allows for an unbiased evaluation of the model’s performance on unseen data, providing a more accurate measure of its generalization capabilities.
2. Overfitting Prevention: By monitoring the model’s performance on the validation set, we can detect and mitigate overfitting, ensuring that the model learns generalizable patterns rather than memorizing the training data.
3. Hyperparameter Optimization: The validation set aids in selecting optimal hyperparameters (e.g., learning rate, batch size) that yield the best model performance.

Integrating Validation with OpenAI’s Fine-Tuning API

OpenAI’s client.fine_tuning.jobs.create API simplifies incorporating a validation dataset into the fine-tuning process. By specifying the validation_file parameter, we can seamlessly integrate the validation data, enabling the API to track the model’s performance on this separate dataset during training.

Refined Fine-Tuning Process

The refined fine-tuning process involves the following steps:

1. Data Preparation: Split the AviationQA dataset into training and validation sets.
2. API Integration: Utilize OpenAI’s client.fine_tuning.jobs.create API, specifying both the training file and the validation file.
3. Monitoring and Evaluation: Monitor the model’s performance on the validation set during training. If overfitting is detected, adjust hyperparameters or employ regularization techniques.
4. Model Selection: Choose the model checkpoint that demonstrates the best performance on the validation set.

Benefits of the Refined Approach

This refined approach offers several advantages:

1. Enhanced Generalization: The model is less prone to overfitting and demonstrates improved generalization to new, unseen aviation-related queries.
2. Optimized Performance: The selection of optimal hyperparameters based on validation set performance leads to a model with superior performance.
3. Increased Confidence: The rigorous evaluation on a separate validation set instills greater confidence in the model’s capabilities.

Case study

I updated a notebook to accommodate the new updates thoroughly tested in Google Colab/Google Cloud to demonstrate how to fine-tune the model gpt-3.5-turbo-0125 using the OPENAI’S API and the dataset AviationQA.

Also, here, you can find the notebook that shows the preparation for the dataset of this fine-tuning.

I am sharing some screenshots(see figures 1–2) from the OPENAI Fine Tuning DashBoard that show some analytics of the new fine-tuned model ft:gpt-3.5-turbo-0125:xamrysoft::9cSMOUWb, including the Testing/Validation/FullValidation loss associated values.

Figure 1: OPEN AI Fine-Tuning Dashboard (view 1)

Figure 2: OPEN AI Fine-Tuning Dashboard (view 2)

I did create a new notebook to evaluate the new fine-tuned model.

Conclusion

By incorporating a validation dataset into the fine-tuning process using OpenAI’s API, we can further enhance the capabilities of the GPT-3.5-turbo model in the aviation domain. This refined approach ensures a more robust, reliable, and high-performing model that addresses various aviation-related queries effectively.