Open AI: Fine Tuning vs Text Embedding
Tags: openai
Published: 2023-06-30 09:59:34

We all know nowadays AI is booming everywhere, and it has divided people in two categories, one in favor of AI and others are against it. Some people are saying it will help people to complete their tasks quickly and others are saying it will make people lazy. But whatever the debate is, I personally feel that it completely depends on the mindset of the person who is using it and utilizing its power in the correct way. It is kind of the same thing as the ‘knife’ if it is in the criminal's hand it may harm people but if it is in the cook's hand, it will help in creating delicious food. Personally I feel it has awesome advantages and it definitely helps software developers like me, and hence I am trying to become more involved in AI and its applications. And as a part of it, this quarter I kept my quarterly SMART goal to understand the difference between two methodologies to train OpenAI with the custom dataset. As we all know that the OpenAI has information available till March 2021 which was publicly available by that time. So now, if you want to train OpenAI with your custom data, it has provided some ways to do it. Fine Tuning and Text Embedding are two of them. Let's understand what they are.

- **Fine-Tuning: Customizing Language Models**
Fine-tuning is a process of training a pre-existing language model on a custom dataset as per user’s requirement to make OpenAI information more suitable for the specific applications. OpenAI's fine-tuning process involves taking a pre-trained base model and training it further on a dataset that is specific to the custom application. Here are some **benefits** of the Fine Tuning training approach.

**Reduces Time for Training** - As we saw in an introduction that the Fine-Tuned models are pre-trained models and hence it allows users to leverage the knowledge and parameters learned during pre-training. And hence it requires less resources and also reduces the time than training the models from the start.

**Transfer Learning** - Again, as the Fine-tuning models are pre-trained, they can learn very effectively and efficiently. This transfer learning allows for better performance even if the target task has limited training data.

**Customization**- Fine tuning allows users the ability to customize models and its behavior according to specific custom requirements. Developers can modify the model’s response or output format as per the requirement of an application. This kind of control over models allows great flexibility and adaptability.

Along with the above advantages, Fine-Tuning also has few **disadvantages** as well, and below are the some of them,

**Limited Generalization** - Even the fine tuning modes are highly optimized for the specific tasks it was trained on but it may lack the broader understanding and generalization capabilities of the original pre-trained model. The fine-tuned model may not perform as well on tasks or domains outside of its limited training scope. 

**Overtraining** - Fine-tuning a model on specific data can end into overtraining, where the model can perform poorly on unseen or slightly different data.

**Dependency on Pre-training** - Fine-tuning is completely dependent on the pre-trained model. If the pre-training process has issues, it can impact the efficiency of the process.

- **Embedding Open AI APIs: Generalized Language Models**
An embedding is a list of some numbers. When we train AI with any information, then each block of data is stored a vector which is nothing but each block represented as some floating number. Text embedding refers to the process of representing text data in a numeric format where words or documents are mapped to vectors. Here are some **benefits** of the Text embedding approach.

**Dimension Reduction** - In general, dimension reduction is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data. Text embedding techniques can effectively reduce the dimension of text data. Instead of representing each word or document as a high dimensional thing, the text embedding compresses the information into lower dimension vectors. 

**Transfer Learning** - Similar to Fine-Tuning, pre-trained text embeddings can be transferred to new tasks or domains with data. This transfer learning speeds up the training process and enhances the performance of models.

**Improved Generalization** - Text Embedding captures the contextual meaning of the words which enables better generalization compared to simple representation of data. This enhances the performance of various natural language processing.

Below are the some of **disadvantages** of Text Embedding,

**Loss of some fine information** - Because of the dimension reduction approach of the Text Embedding, some fine grained information loss. Some characteristics of words or documents may not be captured in the required way in lower dimension vectors. 

**Limited Coverage** - The pre-trained text embeddings may not cover all the words it phrases in given text data.

**Context Related Limitations** - The Text Embeddings depend on the context and the surrounding words in the particular document. This can create a problem of different embedding of the same word or phrase in different documents.

**Summary** - 

It's important to note that the advantages and disadvantages of both Fine-Tuning and Text Embedding can vary depending on the specific use case, dataset, and requirements. Proper understanding of these factors is very important for effective and responsible utilization of these techniques.

 
**Happy Reading!**