Expertise without the Expense: AI Expert Systems for Your Business

IV. Q&A Large Language Model – the Workhorse behind AI Expert Systems

A. Overview of Q&A Large Language Models

Q&A Large Language Models (LLMs) are computer systems that use natural language processing (NLP) and machine learning (ML) techniques to automatically answer questions asked by humans. LLMs are created and fine-tuned using vast amounts of data and are capable of answering a wide range of questions related to a specific domain.

B. Types of Problems and Models

Contracts

1. For contract analysis, LLMs are trained using a large dataset of contract language, which can include standard and custom clauses. LLMs can then answer questions related to the content of the contracts, such as defining terms or explaining how specific clauses affect certain aspects of the contract.

Legal Decisions

2.LLMs can be trained using legal decisions, such as case law, and can provide summaries or explanations of legal precedents or rulings. They can also be used to predict the likelihood of a legal decision based on the specifics of a given case.

Board Meeting Minutes

3. LLMs can be trained to analyze board meeting minutes, and can help identify key themes and topics discussed during meetings. They can also generate summaries of the meeting and provide insights into what was discussed and the outcomes of the meeting.

Emails

4. LLMs can analyze email threads, identify the key topics discussed, and summarize the content of the emails. They can also answer questions related to the content of the emails, such as the meaning of a particular statement or the status of a particular project.

Customer Service Transcripts

5. LLMs can be trained using customer service transcripts and can provide quick and accurate responses to frequently asked questions. They can also be used to identify trends in customer inquiries and provide suggestions for improving customer service.

C.Understanding the Content

To train an LLM, it is important to have a large and diverse dataset that covers the domain of interest. The dataset needs to be pre-processed, which involves removing noise, identifying the key topics and entities, and creating summaries or abstractions of the content. This preprocessing step is critical for creating a high-quality LLM.

D. Models for Questions and Answers

To create a Q&A LLM, two models are typically used: an interrogator model and an expert model. The interrogator model is used to generate questions, while the expert model is used to answer them. The expert model is trained to identify the relevant information from the preprocessed dataset and generate an accurate answer.

E. Expert Models

Expert models are trained on the preprocessed dataset and are designed to be able to answer specific questions related to the domain of interest. The expert model is capable of handling a wide range of questions and can provide accurate answers quickly and efficiently.

F. Benefits of Q&A Large Language Models

LLMs have several benefits over traditional expert systems, including cost savings, faster response times, and improved accuracy. They can be trained on large and diverse datasets and can handle a wide range of questions, making them a valuable tool for businesses and organizations. However, the process of creating and fine-tuning an LLM can be complex and time-consuming. Using an automation tool like Apogee Suite can help streamline the process and make it more efficient.