Natural Language Processing (NLP) has come a long way since its inception in the 1950s. Developers and researchers constantly explore new models that can learn and process language for different applications.

One of the coolest advancements in NLP is the Large Language Model (LLM). It’s totally changed how machines learn to process language. Let’s dive into the history of NLP and how the transformer architecture has played a big role in the growth of generative AI. We’ll also discuss the foundational models that make all this innovation possible. Exciting stuff!

Before we explore the LLM and its place in NLP, let’s review the early history of NLP. In the early days, NLP was a mix of rule-based and statistical approaches. Programs would be designed to follow sets of rules to understand and decode language. As computer hardware advanced, so did the statistical models, and probabilistic models like Hidden Markov Models and Markov Decision Processes became popular.

In the 2010s, the neural network revolution began, and the first LSTMs (Long Short-Term Memory Networks) and CNNs (Convolutional Neural Networks) models appeared. Researchers and developers started realizing that neural networks could be applied to NLP problems with great success. As of 2021, the transformer architecture has become the go-to model for NLP tasks. This totally changed how machines learn the language and process it.

I will compare three cutting-edge foundational language models revolutionizing the field: OpenAI’s GPT-3 and GPT-4, Meta’s LLaMA 2, and Google’s PaLM 2, along with some Open Source projects that will drive this evolution. These powerful models have been trained on vast amounts of text data, enabling them to generate diverse and coherent texts for various tasks and domains. Let’s delve into the key highlights of each model:

GPT-3 and GPT-4: These models were developed by OpenAI, a research organization dedicated to creating Artificial General Intelligence (AGI). GPT-3, released in 2020, boasts an impressive 175 billion parameters, while GPT-4, unveiled in 2022, boasts an undisclosed number of parameters. Built on a transformer architecture and leveraging self-attention mechanisms, both models exhibit remarkable capabilities. Let’s explore some of their key strengths:

• They can generate fluent and natural texts for various domains and tasks, such as summarization, translation, dialogue, question answering, and more.
• They can adapt to different styles, tones, and formats based on the input prompt and the few-shot learning technique.
• They can perform zero-shot and few-shot learning, which means they can learn new tasks without additional training data or labels.

PaLM 2, developed by Google Research, is an advanced version of their previous model, PaLM, released in 2022. Launched in 2023, PaLM 2 introduces four distinct versions: Gecko, Otter, Bison, and Unicorn. This model employs a hybrid architecture that combines transformers and RNNs, leveraging the Pathways Language Modeling (PaLM) technique. Notable strengths of PaLM 2 include its powerful performance, enhanced structure, and improved model capabilities.

• It is integrated with Google’s products and services, such as Bard, Docs, Sheets, Slides, MakerSuite, Vertex AI, and Generative AI App Builder.
• It can support over 100 languages and perform multilingual translation and cross-lingual transfer learning.
• It can generate code from natural language prompts and vice versa.

LLaMA 2, developed by Meta (formerly known as Facebook AI Research or FAIR), is a cutting-edge model introduced in 2023. It boasts three versions: 7 billion, 13 billion, and an impressive 70 billion parameters. Built on a recurrent neural network (RNN) architecture, LLaMA 2 leverages the power of long short-term memory (LSTM) units. This model exhibits several remarkable strengths:

• It is open-source and free, meaning anyone can access and use it for their projects and applications.
• LLaMa 2 has been optimized by humans, making it highly capable and aligned with human preferences. This fine-tuning distinguishes LLaMa 2 from OpenAI’s GPT models¹. LLaMa 2 Chat, a 70 billion-parameter version of LLaMa 2, is fascinating because of its improved capabilities. Also, when it comes to free source LLMs, the Llama 70B Model easily outperforms all other models in all benchmarks
• It can generate multimodal outputs, such as images, videos, audio, and code, in addition to text.

Furthermore, let’s delve into open-source foundational models with commercial licenses. Unlike some of those developed by large tech companies, these models result from independent researchers or organizations striving to make AI more accessible. Here are a few examples of such models:

Alpaca, a model developed by Stanford’s Center for Research on Foundation Models (CRFM), is built upon GPT-3. With a release in 2021 and boasting 10 billion parameters, Alpaca offers an open-source solution. However, non-academic use requires a commercial license. This model comes equipped with a range of impressive features, including:

• It is optimized for low-resource settings and can run on commodity hardware or mobile devices.
• It can perform meta-learning, learning from its mistakes and improving over time.
• It can generate more factual, diverse, and ethical texts than GPT-3.

EleutherAI is a collective of researchers dedicated to developing open-source alternatives to GPT-3. They have introduced several models, including GPT-J (6 billion parameters), GPT-Neo (1.3 billion parameters), GPT-Micro (125 million parameters), and Rotary (175 billion parameters). These open-source models come with different licenses based on the specific model. Let’s explore some remarkable features these models offer: – They are trained on diverse and curated datasets that reduce bias and toxicity.

• Performance: EleutherAI claims that its GPT-J model performs nearly on par with an equivalent-sized GPT-3 model on various tasks¹. GPT-J is a Transformer model, which means it weighs the influence of different parts of input data rather than treating all the input data the same¹.
• Open Source: EleutherAI is a grassroots collection of AI researchers that focuses on noncommercial, open source efforts. Their GPT-NEO model is a series of large language models trained on the Pile dataset and comes in 125M, 1.3B, and 2.7B parameter variants².

Hugging Face: This company provides a platform for building natural language processing (NLP) applications using state-of-the-art models. Hugging Face has released several models, such as BERT (340 million parameters), DistilBERT (66 million parameters), RoBERTa (355 million parameters), and T5 (11 billion parameters). These open-source models require a subscription to the Hugging Face API or the Hugging Face Hub. Some of the features of these models are:

• They are based on pre-trained models that can be easily customized and extended for various NLP tasks.
• They can leverage large-scale datasets and compute resources provided by Hugging Face.
• They can benefit from the community feedback and collaboration on the Hugging Face Hub.

Conclusion:

The Large Language Model, with its huge potential to translate, summarize, and generate text, marks an exciting chapter in the evolution of Natural Language Processing. We must take full advantage of these tools and continue to work diligently to ensure their responsible use as we stride towards more advanced language processing and pervasive automation. On that note, I will follow up on this post with a blog about Ethical considerations, and I will finish with a review of some current applications utilizing foundational models. After we finish those posts, I will discuss how we can build solutions on top of the foundational models and some novel patterns emerging from the development community. We are constantly working with our partners at IntechIdeas to solve novel problems by leveraging innovation. I can’t wait to share and discuss how we can all utilize these new tools.

OpenAI GPT-4 (openai.com)

Meta LLaMa2: Open Foundation and Fine-Tuned Chat Models

Google Research Blog. Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance

Stanford Center for Research on Foundation Models (CRFM). Alpaca: A Strong, Replicable Instruction-Following Model

EleutherAI. “GPT-3’s free alternative GPT-Neo is something to be excited about”

Hugging Face. Transformers: State-of-the-Art Natural Language Processing