Master GPT-3 with ChatGPT: Train on Any Corpus and Unlock Knowledge Graphs!

Table of Contents

1. Introduction
2. The Journey Back
3. Fine-tuning GPT-3
4. The Limitations of Fine-tuning
5. Building a Knowledge Graph
6. Understanding Knowledge Graphs
7. Choosing a File Format
8. Json LD as a Knowledge Graph Format
9. Extracting Nodes from Case Law
10. Summarizing Case Law Opinions
11. Data Preprocessing and Chunking
12. Formatting the Knowledge Graph
13. Experimenting with Knowledge Graph Generation
14. Loading Data into a Database or Visualizer
15. Conclusion

Introduction

In this article, we will explore the concept of building a knowledge graph using GPT-3 and Json LD format. We will Delve into the process of fine-tuning GPT-3, the limitations of fine-tuning, and the potential for building a knowledge graph as a research tool for trial lawyers. Additionally, we will discuss different file formats for knowledge graphs and how to extract nodes from case law. Finally, we will cover data preprocessing, chunking, and the formatting of a knowledge graph.

The Journey Back

After a hiatus, David Shapiro is back and excited to share some news. He addresses the elephant in the room by explaining why he took down most of his videos and repositories, and how striking the balance between creating tools and replacing people is essential. Now that he's back, he plans to get his hands dirty again.

Fine-tuning GPT-3

One frequently asked question is how to fine-tune GPT-3 for training a question-answering bot. However, fine-tuning doesn't work that way. Fine-tuning is about teaching the language model Patterns rather than new knowledge. For example, a pattern for a chatbot could be a question followed by a response, and subsequent follow-up questions triggering additional text generation.

The Limitations of Fine-tuning

While fine-tuning is powerful, it has its limitations. Fine-tuning the underlying model is expensive and not feasible due to the massive amount of data available. This limitation Prompts the exploration of alternative methods for teaching models new knowledge, such as building a knowledge graph.

Building a Knowledge Graph

To build a knowledge graph, we need to understand what it is. A knowledge graph is a data model that represents interconnected data organized around entities and relationships. It allows for efficient querying and visualization of large volumes of structured and unstructured data. We can use tools like Neo4j or Amazon Neptune for this purpose.

Understanding Knowledge Graphs

A knowledge graph consists of nodes representing entities and relationships connected through edges. Each node can have properties such as ID, Type, name, description, and more. These nodes are interconnected through relationships that capture the connections between entities. Json LD is a lightweight linked data format that can be used to represent a knowledge graph.

Choosing a File Format

There are several file formats available for representing a knowledge graph, including GraphML, XML, YEd, RDF, and CSV. Each format has its advantages, such as human readability or compatibility with existing tools. Json LD, in particular, is a popular format for its simplicity and lightweight structure.

Json LD as a Knowledge Graph Format

Json LD is a powerful format for representing a knowledge graph due to its readability and ease of use. Each node in a Json LD knowledge graph contains properties such as ID, type, and description. Relationships between nodes can also be represented using unique identifiers. This format allows for easy manipulation and visualization of the knowledge graph.

Extracting Nodes from Case Law

To build a knowledge graph for case law, we need to extract nodes representing case citations, precedents, and prior opinions. Each node should have properties that include date, case number, involved parties, reasoning for inclusion, and other Relevant information. By organizing case law data into nodes, we can Create a comprehensive knowledge graph for legal research.

Summarizing Case Law Opinions

Given the lengthy nature of case law opinions, it is crucial to summarize them while retaining specific details. By summarizing case law opinions, we can reduce word count and remove superfluous language while still preserving the essential information. Summarization techniques can help ensure the knowledge graph remains concise and informative.

Data Preprocessing and Chunking

Data preprocessing and chunking are crucial steps in preparing case law data for knowledge graph generation. Since some case law opinions can be hundreds of pages long, splitting them into manageable chunks is necessary. This ensures each chunk contains enough information while fitting within the token limit of GPT-3. Additionally, preprocessing steps help clean and format the data for further analysis.

Formatting the Knowledge Graph

The format of the knowledge graph depends on how the data is stored. Json LD provides a structured format for representing nodes and relationships in a knowledge graph. Each node will have unique identifiers and properties, while relationships are established through the connections between nodes. Properly formatting the knowledge graph is essential for its accurate representation and efficient querying.

Experimenting with Knowledge Graph Generation

The process of generating a knowledge graph involves experimenting with GPT-3 to convert case law opinions into Json LD format. By providing instructions to rewrite opinions as a list of assertions, we can extract nodes and relationships from the text. Experimentation allows us to fine-tune the instructions and evaluate the effectiveness of the generated knowledge graph.

Loading Data into a Database or Visualizer

Once the knowledge graph is generated, the next step is to load it into a database or visualizer for easy querying and visualization. Graph databases like Neo4j or Amazon Neptune provide efficient storage and retrieval of the knowledge graph. Visualizers such as graphis or Gephy help visualize and edit the graph, making it easier to navigate and understand.

Conclusion

Building a knowledge graph using GPT-3 and Json LD format offers a powerful tool for legal research and analysis. Despite the limitations of fine-tuning, alternative methods like knowledge graphs provide opportunities to leverage existing data for informative insights. By extracting nodes from case law, summarizing opinions, and building a well-structured knowledge graph, trial lawyers can enhance their trial preparation and gain a deeper understanding of legal Precedent.