Book Image

Mastering NLP from Foundations to LLMs

By : Lior Gazit, Meysam Ghaffari
Book Image

Mastering NLP from Foundations to LLMs

By: Lior Gazit, Meysam Ghaffari

Overview of this book

Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
Table of Contents (14 chapters)

Multiple agents – forming a team of LLMs that collaborate

This section deals with one of the most exciting recent methods in the world of LLMs, employing multiple LLMs simultaneously. In the context of this section, we seek to define multiple agents, each backed by an LLM and given a different designated role to play. Instead of the user working directly with the LLM, as we see in ChatGPT, here, the user sets up multiple LLMs and sets their role by defining a different system prompt for each of them.

Potential advantages of multiple LLM agents working simultaneously

Much like with people working together, here too, we see the advantages of employing several LLMs simultaneously.

Some advantages are the following:

  • Enhancing validation and reducing hallucinations: It has been shown that when providing feedback to an LLM and asking it to reason or to check its response, the reliability of its response improves. When designating roles for the various LLM agents on...