How To Use Large Language Models (LLMs) To Synthesize Training Data

Nowadays, there is an infinite quest for acquiring bias-free, rich, and diverse data in the dynamic realm of AI and ML. But, the matter of worry is that there often comes a multitude of shortcomings such- privacy concerns, scarcity, biases, and a few more. Do you have the strong desire of a world with abundant, unbiased data without any privacy issues? If so, here is the synthetic data one of the game-changing innovations that will definitely reshape the data science landscape.

In which way is it possible to create high-quality synthetic training data? Here is one of the reliable solutions namely- LLMs (large language models). These models are the most powerful tools that can understand, generate, and also refine human-like text. This way, you can also train your models more efficiently. If you are willing to know how you can use LLMs for synthesizing training data, this blog is exactly for you. By delving into this post you may also be able to offer unique solutions to the real-world data challenges, and know in which way you can use LLMs to synthesize your own training data.

Let’s explore the blog thoroughly.

An Overview Of Large Language Models(LLMs)

LLMs are one of a kind artificial intelligence algorithms. And the term generative AI is also interrelated with LLMs. It’s a foundational model that is utilized in NLP (natural language processing) and NLG ( natural language generation) tasks. Besides, these models are known as machine learning models that perform language-related tasks very effectively. With the help of its parameters it’s quite possible to judge the performance level of this model. One of the best things about this model is that it anticipates the text that is likely to come next.

Large Language Model Illustrations

There is a range of LLMs, so let’s have a glance at a few of the prominent ones-

• BLOOM
• GLM-130B
• XLM-RoBERTa
• Cohere
• NeMO LLM
• XLNet
• GPT-3 (Generative Pre-trained Transformer 3)
• T5 (Text-to-Text Transfer Transformer)
• BERT (Bidirectional Encoder Representations from Transformers)
• RoBERTa (Robustly Optimized BERT Pre Training Approach)

Types Of Large Language Models

There are different types of large language models. A few common types among them are-

• Zero-Shot Model- like GPT-3
• Fine-Tuned Or Domain-Specific Models- for example OpenAI Codex
• Language Representation Model- such as Bidirectional Encoder Representations from Transformers (BERT)
• Multimodal Model- for instance GPT-4

Use Cases Of Large Language Models

One of the greatest things about LLMs is that they have a multitude of use cases and are also applicable in various industry verticals. These industries can include- retail, healthcare, fintech, and many more. Here are a few of the use cases of large language models that exist in almost all industries. Let’s have an eye on them all-

• Text translation into other languages
• Improvement in customer experience with AI assistants and chatbots
• Classification, categorization, and organization of customers’ feedback to the concerned departments
• Summarization of large documents such as- legal documents & earning calls
• Creation of new marketing content
• Software code generation from natural language
• Text summarization
• Sentiment analysis
• Text generation
• Speech recognition and synthesis
• Named entity recognition
• Text-to-speech synthesis
• Machine translation
• Fraud detection
• Image annotation
• Code generation
• Spell correction
• Recommendation systems

What Are The Benefits Of Large Language Models?

There are innumerable benefits that large language models provide to users as well as to oragnizations. A few of them are-

• Automation of various processes
• Reduction in manual labour and costs as well
• Enhancement of personalization, and customer satisfaction
• Save business owners’ time
• Increment in accuracy in the tasks
• Create an extensive and adaptable model as per organizations’ specific requirements
• Easy to use for several tasks and deployments across users, applications, and organizations
• Generate rapid, low-latency responses
• Deliver increasing levels of accuracy
• Accelerate the training process
• Enhance AI-powered machines’ capabilities to understand human texts faster and better
• Improve AI-powered machines’ conversational ability
• Facilitate easy cross-cultural communication and breaking down language barriers.

Real-World Applications Of Large Language Models

Do you have enthusiasm to know real-world applications of LLMs, below are some of them-

• Improvement in the users’ search experience
• Provides users with relevant and accurate information
• Enable search engines to understand users’ intent better and represent matching search results
• Generate content faster than humans
• Catch the attention of the writing community
• Helpful to businesses in making content or developing marketing strategies
• Offer a variety of information related to potential users and competitors

How Do LLMs- Large Language Models Work?

The large language model works in the ways mentioned below. So let’s proceed to know about that-

• These models require massive datasets to train AI technology based models. It is essential that these datasets are collected from several sources namely- research papers, blogs, and social media
• It converts the collected data into computer language to train machines more conveniently
• Use different deep-learning techniques to enable training machines to expose the input data
• These models sometimes utilize neural networks to train machines. In the simplest terms neural networks consist of connected nodes that enable the LLM to understand complex relationships between words and the context of the text.

Future Of Large Language Models

Are you wondering about what the coming generation of LLMs would look like? Do you want to have the answer to this question? If so, here is what the future of large language models look like.

• Large language models will generate their training data
• LLM-powered models could replace search engine as it has the capability to respond to any user queries faster
• The next generation of large language models will not likely be artificial general intelligence
• These models will continuously improve and get smarter
• They will continue to be trained on ever larger sets of information

What Does Training Data Mean?

Training data is considered the lifeblood of Machine Learning (ML) systems. It serves as the foundation for the machine learning systems to enable them to make predictions. Such data is one of the crucial pieces to develop ML systems. It’s also been observed that without training data it’s quite impossible to carry out essential tasks.

Training data is the integral component contributing to the success of almost all sorts of AI as well as ML projects. Besides, it’s a key that assists machines to get the actual meaning of human-like behaviour. Training data is helpful in forecasting highly accurate outcomes and dictating the accuracy & performance of AI models.

Significance Of Training Data

Here are a few of the areas where training data plays a significant role. Let’s have an eye on them-

• Helpful in gaining right quality and amount of data
• Plays a crucial role in supervising machine learning
• Recognizes and categorizes objects
• Mandatory for the machine learning algorithms’ operations
• Training data is the key as well as primary input that offer the algorithm the information essential to make decisions equivalent to human intelligence
• Training data verify the machine learning model to evaluate its accuracy and also ensures its application in real-life scenarios

What Does Synthetic Data Refer To?

Synthetic data is not real world data rather it’s a form of data that we create via using computer programs or simulations. It’s just the same as the process of making a twin of a real painting by an artist. So, similarly computer programs make a duplicate copy of patterns found in real data, but without any real data.

Synthetic data is generally used in AI & ML fields. It offers a personalized environment for bringing improvements in ML & AI algorithms. Generally, it’s helpful in mimicking real information but does not enable individuals to control & manipulate to offer exclusive training & test scenarios. Altogether, synthetic data is one of the valuable tools to refine AI & ML models, it’s so because it’s synthetic and enables you to create & customize data as per your requirements.

Types Of Synthetic Data

There are several types of synthetic data that are-

• Synthetic text
• Synthetic media like video, image, or sound
• Synthetic tabular data

What Are Synthetic Data Use Cases?

Below are the use cases of synthetic data, so let’s move ahead to explore them.

• Creates labelled data instances that can be used in training
• Decreases the necessity for time-consuming data labelling efforts
• Prediction of fraud or manufacturing defects
• Increase training data size for ML models
• Allows marketing units to improve their marketing spend
• Beneficial for software testing
• Enables healthcare data professionals to allow the internal and external use of record data

Advantages Of Synthesizing Training Data

There are a range of advantages of synthesizing training data. A few of them are mentioned below-

• Reduction in the risks related to customer data breaches and illegal sharing that may result in high-cost legal battles & harm to brand reputation
• Addresses and minimizes privacy concerns
• Helpful in generating data for completely new products or services in case of absence of historical data related to them
• Offers cost-effective and efficient solutions for new product development and ML model training

Using LLMs For Synthesizing Training Data

The following steps will explain how an large language model can be used for synthesizing data from the model’s training-

Step 1: Select The Right LLM For Your Specific Application

There is a much requirement of considering below mentioned factors while choosing the right LLM to synthesize training data. Let’s have a glance at them.

• Type Of The Task

The task requirements impact the choice of LLM. For instance, for a text generation task- a sequence-to-sequence model and for classification tasks- a simpler model might be the best fit.

• Amount And Quality of Data

Data availability can impact the complexity of the large language model that you pick. The more complex the models the more data they will require for training.

• Requirement For Computational Resources

The more advanced LLMs the more computational memory & prower they will require for training & presumption. So keep this thing in mind that selection of already existing resources is the best option while selecting the model.

• Confidentiality Concerns

If your data involves confidential information, you may require to pick a model that can offer better data privacy.

• Accuracy Versus Explainability

Not all LLMs offer high accuracy as well as explainability. But as per your project requirements you should choose the model. It lets you know the requirement for a simple or interoperable model.

• Amount Of Time Required For Model Training

It takes much time to train a complex LLM. As per the time zone limitations of your project , you might require to go for a less complex model that can be trained more quickly.

Step 2: Training The Model With LLM-Generated Synthetic Data

Let’s train the model with generated synthesized data. It involves several steps, such as-

• Preprocessing the information
• Splitting data into training and testing datasets
• Choosing a model
• Training the model
• Evaluation of the model
• Utilization of anticipation

Here is a clear description of these steps-

Load The Data

Begin by loading your synthetic data.

Preprocess The Data

Before training your model, you will have to preprocess your data. It may include-

1. Converting categorical variables into numeric variables
2. Normalizing numeric variables

Split The Data

You have to split your data into a testing set and a training set. It enables you to evaluate the performance of your model.

Train The Model

Select a suitable machine learning model. For example-RandomForestRegressor or GradientBoostingRegressor.

Evaluate The Model

Make a use of the testing set to evaluate the model’s performance.

Every step mentioned above has a range of possible variations and the best one will rely on business owners’ particular information, problems, and requirements as well. It’s up to the business owners which preprocessing techniques, evaluation metrics and machine learning models they prefer to select.

To summarize,

Large language models allow for new levels of advancements in the understanding and generating text. This way, it has revolutionized the field of NLP (natural language processing). These models can learn from big data, get the exact meaning of its entities & context, and reply to all the queries of users. It makes LLMs one of the greatest alternatives for usage on a daily basis for different tasks in various industry verticals.

One of the major concerns related to the large language models is that there are a few issues associated with these models namely- ethical implications and potential biases. So, it’s significant to approach these models judiciously as well as evaluate their impacts on society. It’s certain that if we use these models carefully, they will bring positive changes in various domains. But one of the most vital things that is a must for us to keep in our mind is limitations & ethical implications of large language models.