Large language models (LLMs) have been gaining traction in the world of natural language processing (NLP) due to their ability to process massive amounts of text and generate accurate results. These models are trained on large datasets, which contain hundreds of millions to billions of words. LLMs, as they are known, rely on complex algorithms including transformer architectures that shift through large datasets and recognize patterns at the word level. This data helps the model better understand natural language and how it is used in context and then make predictions related to text generation, text classification, etc.
This blog post aims to provide a comprehensive understanding of large language models, their importance, and their applications in various NLP tasks. We will discuss how these models work, examples of popular LLMs, and the training process involved in creating them. By the end of this post, you should have a solid understanding of why large language models are essential components for today’s AI applications.
What are large language models (LLM)?
Large Language Models (LLMs) are a class of deep learning models designed to process and understand vast amounts of natural language data. Simply speaking, large language models can be defined as machine learning models that try to solve text-generation tasks (primarily) thereby enabling more effective human-machine communication. This is why LLMs need to process & understand huge volume of text data and learn patterns and relationships between words in sentences. The GPT-4 and ChatGPT are advanced LLMs that demonstrate exceptional performance in generating text for various tasks.
LLMs are built on neural network architectures, particularly the transformer architecture, which allows them to capture complex language patterns and relationships between words or phrases in large-scale text datasets. As a matter of fact, LLM can also be understood as variants of transformer. The transformer architecture relies on the mechanisms such as cross-attention and self-attention, which allows the model to understand the relationship between words in a text by weighing the importance of different words or phrases in a given context. The cross-attention mechanism enables the model to identify the significant portions of an input text necessary for accurately predicting the next word in the generated text. On the contrary, self-attention mechanism refers to the model’s capability to selectively attend to various sections of its input during processing.
Recall that the transformer architecture represents the neural network model for natural language processing tasks based on encoder-decoder architecture, which was introduced in the paper “Attention Is All You Need” by Vaswani et al. in 2017. The key component of the transformer architecture is the self-attention mechanism, which enables the model to attend to different parts of the input sequence to compute a representation for each position. The transformer consists of two main components: the encoder network and the decoder network. The encoder network takes an input sequence and produces a sequence of hidden states, while the decoder network takes a target sequence and uses the encoder’s output to generate a sequence of predictions. Both the encoder and decoder are composed of multiple layers of self-attention and feedforward neural networks. The picture given below represents the original transformer architecture.
Different types of LLMs
There are three main types of large language models (LLMs) based on the transformer architecture:
- Autoregressive Language Models (e.g., GPT): Autoregressive models generate text by predicting the next word in a sequence given the previous words. They are trained to maximize the likelihood of each word in the training dataset, given its context. The most well-known example of an autoregressive language model is OpenAI’s GPT (Generative Pre-trained Transformer) series, with GPT-4 being the latest and most powerful iteration.
- Autoencoding Language Models (e.g., BERT): Autoencoding models, on the other hand, learn to generate a fixed-size vector representation (also called embeddings) of input text by reconstructing the original input from a masked or corrupted version of it. They are trained to predict missing or masked words in the input text by leveraging the surrounding context. BERT (Bidirectional Encoder Representations from Transformers), developed by Google, is one of the most famous autoencoding language models. It can be fine-tuned for a variety of NLP tasks, such as sentiment analysis, named entity recognition, and question answering.
- Third one is the combination of autoencoding and autoregressive such as T5 model.
Real-life Use Case Scenarios for LLMs
While traditional NLP algorithms typically only look at the immediate context of words, LLMs consider large swaths of text in order to better understand the context. Here are two example scenarios showcasing the use of autoregressive and autoencoding large language models for text generation and text completion, respectively.
Lets take an example of how autoregressive models work. As learned earlier, the autoregressive models such as GPT, generates a coherent and contextually relevant sentence based on the given input prompt.
Let’s say the input to the autoregressive model is the following:
“Introducing new smartphone, the UltraPhone 3000, which is designed to”
The generated text can be:
“redefine your mobile experience with its cutting-edge technology and unparalleled performance.”
Lets take another example of how autoencoding models work. As learned earlier, the autoencoding models, such as BERT, is used to fill in the missing or masked words in a sentence, producing a semantically meaningful and complete sentence.
Lets say the input to the autoencoding model is the following:
The latest superhero movie had an _______ storyline, but the visual effects were _______.
The completed text will look like the following:
The latest superhero movie had an decent storyline, but the visual effects were mind-blowing.
How does LLM work? Key Building Blocks
The following is an overview of some of the critical components:
- Tokenization: Tokenization is the process of converting a sequence of text into individual words, subwords, or tokens that the model can understand. In LLMs, tokenization is usually performed using subword algorithms like Byte Pair Encoding (BPE) or WordPiece, which split the text into smaller units that capture both frequent and rare words. This approach helps to limit the model’s vocabulary size while maintaining its ability to represent any text sequence.
- Embedding: Embeddings are continuous vector representations of words or tokens that capture their semantic meanings in a high-dimensional space. They allow the model to convert discrete tokens into a format that can be processed by the neural network. In LLMs, embeddings are learned during the training process, and the resulting vector representations can capture complex relationships between words, such as synonyms or analogies.
- Attention: Attention mechanisms in LLMs, particularly the self-attention mechanism used in transformers, allow the model to weigh the importance of different words or phrases in a given context. By assigning different weights to the tokens in the input sequence, the model can focus on the most relevant information while ignoring less important details. This ability to selectively focus on specific parts of the input is crucial for capturing long-range dependencies and understanding the nuances of natural language.
- Pre-training: Pretraining is the process of training an LLM on a large dataset, usually unsupervised or self-supervised, before fine-tuning it for a specific task. During pretraining, the model learns general language patterns, relationships between words, and other foundational knowledge. This process results in a pretrained model that can be fine-tuned using a smaller, task-specific dataset, significantly reducing the amount of labeled data and training time required to achieve high performance on various NLP tasks.
- Transfer learning: Transfer learning is the technique of leveraging the knowledge gained during pretraining and applying it to a new, related task. In the context of LLMs, transfer learning involves fine-tuning a pretrained model on a smaller, task-specific dataset to achieve high performance on that task. The benefit of transfer learning is that it allows the model to benefit from the vast amount of general language knowledge learned during pretraining, reducing the need for large labeled datasets and extensive training for each new task.
Examples of Large Language Models (LLMs)
Large language models can be used for a variety of tasks such as sentiment analysis, question answering systems, automatic summarization, machine translation, document classification, text generation, and more. For example, an LLM could be trained on customer reviews to identify sentiment in those reviews or answer questions about the products or services offered by the company based on customer feedback. Additionally, an LLM could be used to generate summaries of lengthy documents or translate them into another language. Furthermore, an LLM could also be used to classify documents into different categories based on their content or generate entirely new text based on existing texts.
Here are some examples of large language models:
- Turing NLG (Microsoft)
- Gopher, Chichilla (Deepmind)
- Switch transformer, GLAM, PALM, Lamba, T5, MT5 (Google)
- OPT, Fairseq Dense (Meta)
- GPT-3 versions such as GPT-Neo, GPT-J, & GPT-NeoX (Open-AI)
- Ernie 3.0 (Baidu)
- Jurassic (AI21Labs)
- Exaone (LG)
- Pangu Alpha (Huawei)
- Roberta, XML-Roberta, Deberta
White Papers for Learning Large Language Models
White papers are an excellent resource for gaining an in-depth understanding of the concepts and advancements in the field of large language models. From the development of neural machine translation to the latest pre-training methods for natural language generation and comprehension, these papers provide a comprehensive view of the evolution of language models. This following list includes some of the most influential papers in the field:
- Neural Machine Translation by Jointly Learning to Align and Translate (2014) by Bahdanau, Cho, and Bengio, https://arxiv.org/abs/1409.0473
- Attention Is All You Need (2017) by Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, and Polosukhin, https://arxiv.org/abs/1706.03762
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018) by Devlin, Chang, Lee, and Toutanova, https://arxiv.org/abs/1810.04805
- Improving Language Understanding by Generative Pre-Training (2018) by Radford and Narasimhan, https://www.semanticscholar.org/paper/Improving-Language-Understanding-by-Generative-Radford-Narasimhan/cd18800a0fe0b668a1cc19f2ec95b5003d0a5035
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension (2019), by Lewis, Liu, Goyal, Ghazvininejad, Mohamed, Levy, Stoyanov, and Zettlemoyer, https://arxiv.org/abs/1910.13461
- Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond (2023) by Yang, Jin, Tang, Han, Feng, Jiang, Yin, and Hu, https://arxiv.org/abs/2304.13712
Presentation on Large Language Models
Here is a set of slides for quick learning on concepts of large language models. The content of slides are aligned with the content of this blog.
Large language models are powerful tools for processing natural language data quickly and accurately with minimal human intervention. These models can be used for a variety of tasks such as text generation, sentiment analysis, question answering systems, automatic summarization, machine translation, document classification and more. With the LLMs’ ability to quickly and accurately process vast amounts of text data, they have become invaluable tools for various applications across different industries. NLP researchers and specialists should definitely familiarize themselves with large language models if they want to stay ahead in this rapidly evolving field. All in all, large language models play an important role in NLP because they enable machines to better understand natural language and generate more accurate results when processing text. By utilizing AI technology such as deep learning neural networks, these models can quickly analyze vast amounts of data and deliver highly accurate outcomes that can be used for various applications in different industries.