Using Large Language Transformer Models for Research in R: A Short Course

Why are Large Language Transformer Models (LLMs) so popular nowadays?

Large language transformer models, such as GPT-4, have gained popularity for several reasons:

1. State-of-the-art performance: These models have achieved state-of-the-art performance on a wide range of natural language processing tasks, including language translation, text summarization, question answering, and language generation.
2. Zero-shot learning: LLMs can perform tasks for which they have not been explicitly trained, a property known as zero-shot learning. This is because they have been trained on a vast amount of diverse text data, allowing them to understand the underlying patterns and relationships in natural language.
3. Scalability: LLMs are highly scalable and can be fine-tuned for specific tasks with relatively small amounts of task-specific data.
4. General-purpose: LLMs are designed to be general-purpose, meaning they can be used for a wide variety of natural language processing tasks without the need for specialized models for each task.
5. Ease of use: Many LLMs are available as pre-trained models, allowing developers and researchers to use them without the need for extensive training or expertise in natural language processing.

Overall, the combination of state-of-the-art performance, zero-shot learning, scalability, general-purpose design, and ease of use make large language transformer models highly attractive for a wide range of natural language processing applications.

Our course is designed as a first introduction to natural language processing and large language models for research applications, covering some basic concepts and applications of transformer models in R.

This is a hands-on course with instructor-led software demonstrations and guided exercises. These guided exercises are designed for the R language, so you should use a computer with a recent version of R (version 4.1.3 or later) and RStudio (version 2022.02.1+461 or later).

To follow along with the course exercises, you should have good familiarity with the use of R, including opening and executing data files and programs, as well as performing very basic data manipulation and analyses.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.

The course is designed for participants who have a solid basic understanding of R and are interested in applying NLP techniques to extract insights from unstructured text data for research purposes.

Introduction to text mining

• What is text mining
• Common applications of text mining
• From texts to structured data
• Overview of the process of converting unstructured text data to structured data
• Text tokenization, stop word removal, and stemming
• Transforming text data into a usable format for modeling

Word embeddings

• Introduction to word embeddings and their use in text classification
• Different types of word embeddings

Introduction to large language transformer models: Understanding the concept of zero-shot classification

• Introduction to large language transformer models
• Understanding the difference between traditional NLP models and large language transformer models
• Introduction to Hugging Face Transformers and its implementation in R
• Research examples of zero-shot classification using R

Automatic text classification and summarization

• Automatic text classification and summarization using R