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ChatGPT - Benefits for Law Firms - Part 1 of 4 - Legal Research

February 2023

ChatGPT is a state-of-the-art language model developed by OpenAI. It uses deep learning algorithms to generate human-like responses to text-based inputs. The model was trained on a large corpus of text from the internet, including websites, books, and other written materials, and has the ability to generate responses in a wide range of styles and formats, including natural language text, code, and other forms of structured data.

Related: ChatGPT - Benefits for Law Firms - Part 1 of 4 - Legal Research , ChatGPT - Benefits for Law Firms - Part 2 of 4 - Legal Document Drafting and Review , ChatGPT - Benefits for Law Firms - Part 3 of 4 - Legal Contract Review

ChatGPT has a number of applications, including natural language processing, conversation systems, language translation, text summarization, and document generation. It can be used to automate tasks such as customer service, content creation, and research.

Law Firms are using ChatGPT in a number of ways. In this 4 part series we will target different ways that Inherent, Inc. is helping Law Firms benefit from ChatGPT. In this articles we will focus on Legal Research.

ChatGPT can assist lawyers with legal research by answering questions and providing relevant information quickly.

How does ChatGPT do its research to generate answers?

ChatGPT, as a language model developed by OpenAI, was trained on a large corpus of text from the internet, which includes a wide range of information, including legal websites, legal databases, and legal textbooks. During its training, the model learned patterns and relationships within the text data, and it uses that information to generate responses to new text inputs.

It's important to note that while ChatGPT has been exposed to a wide range of information during its training, the specific information it provides in response to a user's input will depend on the context of the input and the specific information available within its training data. 

GIGO or Garbage In Garbage Out, and how do I get the best repsonses from ChatGPT?

When asking ChatGPT a question or providing it with a prompt, it's important to be as clear and concise as possible. Providing the model with well-structured and detailed inputs can help it generate more accurate and relevant responses.

Here are a few examples of concise legal questions that you can ask ChatGPT:

  • "What is the statute of limitations for filing a personal injury claim in California?"
  • "What is the standard of proof for a criminal case in the US?"
  • "What are the elements of a valid contract under US law?"
  • "What is the definition of insider trading under SEC regulations?"
  • "What is the process for filing a patent application in the US?

It can also be helpful to provide relevant laws, cases, or other legal information to give the model context for its response. Below are some examples.

  • "What are the rights of employees under the Fair Labor Standards Act (FLSA)?"
  • "What is the standard of proof for a criminal case in the US?"
  • "What is the maximum sentence for a violation of the Securities Exchange Act of 1934?

Fine-tuning and Customizing ChatGPT: ChatGPT is trained using a variant of the Transformer architecture and a deep learning technique called unsupervised learning. The training process involves exposing the model to a large corpus of text and allowing it to learn patterns and relationships within the data.

While ChatGPT is powerful in its out of the box state, it's most effective, when it is customized and trained specifically for the organization that is using it. That is where Inherent, Inc. can come in to help build your custom application. Contact us here for more information.

Here's a high-level overview of the Customizing and Training Process:

  1. Data collection: The first step in training ChatGPT is to collect a large corpus of text data. This data typically comes from a variety of sources, including websites, books, and other written materials.

  2. Preprocessing: The next step is to preprocess the data to make it suitable for training the model. This may include cleaning and normalizing the text, removing irrelevant or duplicate data, and converting the text into numerical representations suitable for use by deep learning algorithms.

  3. Model architecture: The next step is to define the model architecture. This includes specifying the number of layers, the size of the model, and the type of activation functions to be used.

  4. Training: The model is then trained using a variant of the Transformer architecture and a deep learning technique called unsupervised learning. During training, the model is exposed to the preprocessed text data and adjusts its parameters to minimize the difference between its predicted outputs and the actual text in the data.

  5. Fine-tuning: After the initial training, the model can be fine-tuned on specific tasks, such as natural language generation or answering questions, to improve its performance for those tasks.

  6. Evaluation: The final step is to evaluate the model to see how well it performs on a set of benchmark tasks. This allows researchers to assess the model's strengths and weaknesses and make any necessary adjustments to the model architecture or training process.

The training process for ChatGPT can take several weeks or even months, depending on the size and complexity of the model and the amount of data used for training. Additionally, training large language models like ChatGPT requires significant computational resources, including GPUs and high-performance computing clusters.

If you are interested in learning about how Inherent & ChatGPT can help your Law Firm, please Contact Us for more information.