Large Language Models (LLM) have gained attention in artificial intelligence recently. This article explains what LLM is with several examples.
- 1 What is a Large Language Model?
- 2 What techniques do LLMs use?
- 3 Examples of LLMs
- 4 Strengths and Limitations of LLMs
- 5 Responsible AI
What is a Large Language Model?
A Large Language Model (LLM) is a machine learning model designed to understand and generate human language. It is a class of neural network models trained on large amounts of text data, such as books, articles, and other written documents, to learn the statistical patterns and relationships between words and phrases in natural language.
The following video provides examples of LLMs.
What techniques do LLMs use?
LLMs use various techniques to model language, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers. These models are typically composed of many layers, with millions or even billions of parameters, allowing them to capture complex patterns in language and generate realistic and coherent text.
Examples of LLMs
Several Large Language Models (LLMs) have been developed in recent years. Here are some of the most prominent ones:
GPT-3 (Generative Pre-trained Transformer 3)
It is developed by OpenAI. GPT-3 is one of the largest and most advanced LLMs currently available. It has over 175 billion parameters. It is capable of generating highly coherent text. GPT-3 has been used for a wide range of applications, such as chatbots, content creation, and language translation.
There has been a buzz around ChatGPT. Many confuse ChatGPT and GPT-3 to be the same. They are actually not the same. While ChatGPT and GPT-3 share similarities in their underlying architecture, they are distinct models with different characteristics. ChatGPT is designed specifically for conversational AI, while GPT-3 is a more general-purpose language model that can be applied to various natural language processing tasks.
BERT (Bidirectional Encoder Representations from Transformers)
It is developed by Google. BERT is a powerful LLM capable of understanding the context of words and phrases in natural language. It has been used for various applications, including question-answering and sentiment analysis. One of my Ph.D. students uses BERT-generated vectors in his research to create coherent stories from news articles.
XLNet (eXtreme MultiLingual Language Model)
It was developed by Carnegie Mellon University and Google. XLNet is an LLM that uses an autoregressive model to generate text. It can generate high-quality text in multiple languages and has been used for applications such as language translation and content creation.
T5 (Text-to-Text Transfer Transformer)
A lot of T’s, not really, five trees. Developed by Google, T5 is an LLM capable of generating a wide range of natural language outputs, including translation, summarization, and question-answering. It has been used for applications such as language modeling and conversational agents. A more advanced version of T5 is already released and is called T5X.
RoBERTa (Robustly Optimized BERT pre-training Approach)
All big tech companies need an LLM nowadays, of course, having one brings prestige. So, Facebook, sorry, Meta, needs one too. Yes, Meta’s LLM is RoBERTa. RoBERTa is an LLM that builds on the BERT architecture to improve performance on various natural language processing tasks.
Strengths and Limitations of LLMs
These are just a few examples of the several LLMs that have been developed in recent years. Each of these models has its strengths and weaknesses, and the choice of model will depend on the specific application and task at hand. Please feel free to write more names of LLMs in the comments section below.
The main advantage of LLMs is their ability to generate natural language text that is contextually relevant and grammatically correct. This has led to the developing of applications such as chatbots, virtual assistants, research summarization, and content creation tools. LLMs have also been used for advanced natural-language-processing tasks such as question-answering, text summarization, knowledge acquisition, and sentiment analysis.
However, training LLMs requires vast amounts of text data and computing resources, which can be prohibitively expensive for many applications. LLMs can sometimes generate biased language, which can have negative consequences if not carefully monitored.
Despite these challenges, LLMs represent a significant breakthrough in natural language processing and are likely to impact many industries in the coming years profoundly. The utilization of LLMs in applications falls under Responsible AI, which refers to the ethical, transparent, and trustworthy development and deployment of artificial intelligence (AI) systems. As machine learning researchers and practitioners, we should ensure that AI technologies are designed and implemented to align with human values and societal norms.
Enjoy the beauty of artificial intelligence.