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Introducing Synthetic Task Generation with ChatGPT in NLP Lab 5.2

The future of Natural Language Processing (NLP) is here, and it’s more flexible and user-friendly than ever before. NLP Lab, a pioneering No Code Platform designed for document annotation and model training, has just rolled out a powerful new feature – Synthetic Task Generation, integrated with ChatGPT. Let’s delve into this exciting update and how it promises to change the way we handle and analyze data in NLP Lab.

The Problem: Insufficient and Skewed Data

Every data scientist knows the pain of working on a project with a limited or skewed dataset. When there are not enough documents available for a given project or when the data is skewed, creating a robust model becomes challenging. These biases can lead to the misrepresentation of certain entities, making it difficult to develop models that cover the full spectrum of entity descriptions in text. Enter Synthetic Task Generation!

NLP Lab Meets ChatGPT

The integration of ChatGPT with NLP Lab allows for the creation of synthetic data that can address the issues of insufficient and skewed data. This collaboration brings the power of ChatGPT’s text generation capabilities right into the NLP Lab’s intuitive interface.

Admin Control and Integration

The admin user can effortlessly define integration with an external service like ChatGPT from the System settings page. During the integration process, Each Service Provider Key can be validated via the UI (User Interface), ensuring seamless integration.

The service can then be activated for projects that require synthetic data generation.

This offers the flexibility to customize the import feature according to the unique requirements of each project. It also allows the reuse of the service integration across the enterprise, by permitting multiple teams to share the same API Key while also restricting access to experimental or non-essential projects that do not require it, for cost-effectiveness.

Generating Batch Tasks

One of the standout features is the ability to generate batch tasks through the import page. Users need to provide a prompt, which can be tested via the ChatGPT interface and copied/pasted into NLP Lab when ready, and then tune the temperature and the number of results to generate.

The “Temperature” parameter governs the “creativity” or randomness of the LLM-generated text. Higher temperature values (e.g., 0.7) yield more diverse and creative outputs, whereas lower values (e.g., 0.2) produce more deterministic and focused outputs.

This ensures that the generated data aligns well with the project’s specific goals and criteria.

Editing, Tagging, and Exporting

The NLP Lab integration delivers the generated texts in a dedicated UI that allows users to review, edit, and tag them in place. This offers an extra layer of manual control over the results that ensures the quality and relevance of the synthetic data.

And that’s not all! The results can be exported in CSV format for further exploitation. Whether you need to share the results with other team members or want to utilize them in another application, the export functionality has you covered.

Continuous Annotation Process

The generated results can be imported as new tasks, allowing users to continue the annotation process. This facilitates an ongoing iterative process where synthetic data can be continually refined and enhanced, providing a dynamic and responsive approach to data modeling.


NLP Lab’s new feature of Synthetic Task Generation with ChatGPT is more than just a novel addition; it’s a leap forward in how we address the challenges of limited and skewed data. With easy integration, customizable settings, and robust editing and export options, it brings more flexibility and control to the world of NLP.

Whether you are an experienced data scientist or just starting your journey in NLP, this new feature opens up opportunities to explore, experiment, and excel.

Getting Started is Easy

The NLP Lab is a free text annotation tool that can be deployed in a couple of clicks on the AWS, Azure or OCI Marketplaces, or installed on-premise with a one-line Kubernetes script.
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