How to generate error-free structured data automatically using LLMs

Key Takeaways

• The article explains how to use large language models (LLMs) to generate structured data, such as JSON documents, that match a specific schema or format.
• The article discusses some of the methods, benefits, and challenges of using LLMs for structured data generation, and provides some resources and tools to help you get started.

Problem

Creating structured data manually can be tedious and error-prone, especially when dealing with complex and nested structures. Fortunately, there is a way to automate the process of generating structured data using large language models (LLMs). LLMs are deep learning models that can understand and generate natural language texts, such as GPT-4 and ChatGPT. LLMs can also be used to generate structured data, such as JSON documents, that match a specific schema or format. In this article, we will show you how to use LLMs to generate complex and nested JSON documents that conform to a specific schema. We will also discuss some of the benefits and challenges of using LLMs for structured data generation, and provide some resources and tools to help you get started.

One of the main challenges of generating structured data with LLMs is to ensure that the output conforms to a specific schema or format. To generate structured data that matches a schema, we need to provide the LLM with some information about the schema and the desired output. There are different ways to do this, depending on the type and complexity of the schema and the output.

Method 1: Fine-tuning the model on a dataset of valid JSON examples

This method involves pre-training the LLM on a diverse dataset of JSON documents that match the target schema. This allows the model to learn the syntactic patterns and valid nesting structures of the schema. Then, we can use the fine-tuned model to generate new JSON documents by providing a prompt or a partial input. For example, we can ask the model to generate a JSON document representing a blog post that conforms to a specific schema, and provide some keywords or categories as the input. The model will then generate a JSON document that fills in the missing fields and values, based on the learned schema and the input.

Method 2: Creating a context-aware prompt

This method involves creating a prompt that provides the model with context about the JSON structure and schema. For example, we can write a sentence or a paragraph that describes the schema and the output we want, and use it as the input for the model. The model will then generate a JSON document that follows the instructions and the schema provided in the prompt. For example, we can write: “Generate a JSON document representing a blog post that conforms to this schema. { “title”: string, “content”: array of { “type”: “paragraph”|”image”|”embed”, “text”: string } } JSON:” and the model will generate a JSON document that matches the schema and the input.

Method 3: Using hierarchical decoding

This method involves breaking the generation process into multiple steps, by first generating the top-level fields, then recursively generating the content for each field. This allows us to validate and control the output at each step, and ensure that it conforms to the schema. For example, we can first generate the names and types of the fields at the top level of the JSON document, such as “title”, “content”, and “type”. Then, we can generate the values and subfields for each field, such as the title text, the content array, and the type of each element in the array. We can use different prompts or inputs for each step, depending on the schema and the output we want.

Benefits of using LLMs for structured data generation

Using LLMs for structured data generation can offer several benefits, such as:

• Reducing manual effort and errors: Generating structured data manually can be tedious and error-prone, especially when dealing with complex and nested structures. Using LLMs can automate and simplify the process, and produce valid and consistent outputs that match the schema and the input.
• Increasing data diversity and quality: Generating structured data with LLMs can create more diverse and realistic data sets, that can capture the variability and complexity of real-world data. This can improve the quality and performance of data analysis and machine learning applications that use the generated data as input or training data.
• Enabling data privacy and security: Generating structured data with LLMs can help protect the privacy and security of sensitive or confidential data, such as personal or financial information. Instead of using real data that may contain identifiable or risky information, we can use synthetic data that mimics the characteristics and patterns of the real data, but does not reveal any sensitive details.

Challenges of using LLMs for structured data generation

However, using LLMs for structured data generation can also pose some challenges, such as:

• Ensuring data accuracy and validity: Generating structured data with LLMs can introduce errors and inconsistencies, especially when the schema or the output is complex or ambiguous. For example, the model may generate invalid or out-of-range values, duplicate or missing fields, or incorrect or incompatible types. Therefore, it is important to validate and verify the output, and use appropriate methods and tools to ensure that the output conforms to the schema and the input.
• Maintaining data relevance and usefulness: Generating structured data with LLMs can create data that is irrelevant or useless for the intended purpose or application. For example, the model may generate data that does not match the domain or the context of the problem, or data that does not provide any new or useful information. Therefore, it is important to evaluate and measure the output, and use appropriate metrics and criteria to ensure that the output is relevant and useful for the intended purpose or application.

Resources and tools to help you get started with LLMs for structured data generation

If you are interested in learning more about LLMs and how to use them for structured data generation, here are some resources and tools that you can explore:

• GenAI Stack Exchange: This is a question and answer site for artificial intelligence enthusiasts and professionals. You can ask and answer questions about LLMs and structured data generation, and learn from the community of experts and learners.
• Mockaroo: This is a web-based tool that lets you generate up to 1,000 rows of realistic test data in CSV, JSON, SQL, and Excel formats. You can customize the data fields, types, and values, and use the tool to generate structured data that matches your schema and requirements.
• ChatGPT: This is a web-based tool that lets you interact with a chatbot powered by GPT-4, a state-of-the-art LLM. You can use the tool to generate natural language texts, such as sentences, paragraphs, or articles, based on your input and prompt. You can also use the tool to generate structured data, such as JSON documents, by providing a context-aware prompt that describes the schema and the output you want.

Frequently Asked Questions (FAQs)

Here are some frequently asked questions about LLMs and structured data generation:

Question: What are LLMs and how do they work?

Answer: LLMs are deep learning models that are trained on a large amount of text data, such as books, articles, and web pages. LLMs can learn the patterns and rules of natural language, such as grammar, syntax, and semantics, and use them to generate new texts that are coherent and fluent. LLMs can also learn from different domains and genres of texts, such as fiction, news, or technical writing, and adapt their style and tone accordingly.

LLMs work by using a neural network architecture called a transformer, which consists of multiple layers of attention mechanisms. Attention mechanisms are mathematical functions that allow the model to focus on the most relevant parts of the input and output sequences, and learn the relationships between them. For example, when generating a sentence, the model can use attention to select the most appropriate words based on the previous words and the context.

LLMs can generate texts in two ways: autoregressive and non-autoregressive. Autoregressive LLMs generate texts one word at a time, from left to right, based on the previous words. Non-autoregressive LLMs generate texts in parallel, by predicting multiple words at once, based on the entire input. Autoregressive LLMs tend to produce more fluent and coherent texts, but are slower and more computationally expensive. Non-autoregressive LLMs tend to produce faster and more diverse texts, but are more prone to errors and inconsistencies.

Question: What is the difference between structured data and unstructured data?

Answer: Structured data is data that is organized and formatted in a way that makes it easily searchable and readable by data analysis tools, such as JSON, XML, or CSV. Unstructured data is data that does not have a predefined structure or format, such as videos, images, or text.

Question: What are some examples of structured data generation with LLMs?

Answer: Some examples of structured data generation with LLMs are:

• Generating JSON documents that represent blog posts, products, or reviews, based on a specific schema and input.
• Generating SQL queries that retrieve or manipulate data from a database.
• Generating XML documents that represent books, articles, or recipes, based on a specific schema and input.
• Generating CSV files that contain data from various sources, such as web scraping, surveys, or APIs.

Summary

In this article, we have learned how to use LLMs to generate structured data, such as JSON documents, that conform to a specific schema. We have discussed some of the methods, benefits, and challenges of using LLMs for structured data generation, and provided some resources and tools to help you get started. We hope that this article has inspired you to explore the possibilities and applications of LLMs and structured data generation.

Disclaimer: This article is for informational purposes only and does not constitute professional advice. The use of LLMs and structured data generation may involve legal, ethical, and technical issues that require careful consideration and evaluation. The author and the publisher are not responsible for any consequences or damages that may arise from the use of LLMs and structured data generation. The user is solely responsible for verifying the accuracy, validity, and relevance of the generated data, and for complying with the applicable laws and regulations.