You’re at a dinner party when the conversation takes a computer-science-y turn.

• Have you tried ChatGPT?
• What do you think of generative AI?
• Are we all about to be replaced by robots? 

Someone might reply that yes, ChatGPT is going to change everything — and it’s already made some fairly mind-blowing inroads, don’t you think?

But while ChatGPT has gotten most of the attention so far, it’s not alone. It’s only one of a robust group of advanced artificial intelligence models called large language models (LLMs), which are designed to comprehend and generate human language. Together, these large models are significantly impacting the field of natural language processing (NLP) and demonstrating remarkable data-science capabilities in understanding and generating human language. And as research and development continues, you can expect even more groundbreaking developments in the ways large language models work.

In other words, there’ll be plenty more AI-focused dinner chats.

With that outlook in mind, and so you can hold your own during the main course, here’s a little background on LLMs and some of the best examples of the leaders that are pushing the AI envelope.  

What are large language models? 

At their core, LLMs are made up of a huge number of trainable variables, or parameters. An LLM is first trained — fattened up on vast portions of training data (input text). The parameters imbibe the essence of language through exposure to enormous datasets that comprise text from the various sources. Each parameter ultimately adjusts and aligns itself through iterative learning processes.

Reinforcement learning from human feedback is applied, and the model’s proficiency is gradually enhanced. The trained model utilizes complex algorithms to learn patterns, relationships, and semantic meanings within language to ensure expert text generation. Over time, it not only recognizes syntax and grammar but gains insight on nuanced relationships and semantic intricacies embedded in the language.

The genius of LLMs lies in their utilization of deep learning techniques. These models employ specialized neural networks known as transformers (not the Megatron kind). The transformer architecture has proven to be remarkably effective in handling text data. Transformer-based models (see Attention Is All You Need) process and analyze data, allowing the provision of coherent, relevant responses. Through layers of attention mechanisms and normalization mechanisms, transformer models empower LLMs to cut through the complexities of language, providing the ability to generate text that’s not only grammatically correct but contextually relevant and meaningful.

Large-language-model applications

LLMs are being integrated with various domain-specific technologies to provide solutions once considered the stuff of science fiction. They’re instrumental in machine translation and in breaking down global language barriers through multilingual communication abilities. In the realm of sentiment analysis, LLMs can gauge the emotional tone of text, providing insights for businesses looking to understand their customers’ needs. 

• They power question-answering systems and conversational AI chatbots, offering instant support and information.
• They instantaneously produce general-purpose articles, reports, email, and sales pitches, all from just entering simple commands. 
• Some LLMs are “foundation models” (see Wikipedia), general-focus base architectures that can subsequently be tailored for various use cases and specific tasks.

These complex systems represent serious advancement in AI: they’re behind the quantum leap in the capabilities of AI models, pushing the boundaries of what’s possible in a wide variety of arenas.

Large language models examples 

What are the names of these star players? Here’s our list of aspiring large language models, which includes some of the most promising contenders at the time of this writing. 

GPT-3

OpenAI’s Generative Pre-trained Transformer 3 is one of the most remarkable language models to date. At the time of its release, the GPT-3 model comprised an unprecedented 175 billion parameters. Its use spans language translation and summarization to chatbot development and creative-writing assistance. With a Python programming language interface available, developers around the globe have harnessed its capabilities for diverse projects, many of which can be found on GitHub.

GPT-3.5 

Building on the foundation of its predecessors, this updated OpenAI version offers enhanced ability to generate coherent and contextually relevant text, making it even more effective for a wide range of language-related tasks. GPT-3.5 maintains flexibility, allowing it to be applied to content generation, math equations, the explanation of complex ideas, language translation, and more. By fine-tuning capabilities, GPT-3.5 represents a significant step in the field of NLP, enabling even-more-sophisticated language generation.

GPT-4

The largest model in OpenAI’s GPT series, Generative Pre-trained Transformer 4 was released in 2023. Like other LLMs, it’s a transformer-based model. The key differentiator is that its parameter count is more than 170 trillion. It can easily process and generate both language and images, and it can analyze data and produce graphs and charts. It features a system message that lets users specify tone of voice and task. It also powers Microsoft Bing’s AI chatbot. 

BERT (Bidirectional Encoder Representations from Transformers) 

BERT has gained significant attention for its ability to understand the context and nuances of language. It’s been employed in NLP tasks such as sentiment analysis, named entity recognition, and question answering systems. It has also provided substantial improvements in language understanding by capturing complex relationships between words and phrases. 

Bard 

Developed by Google, Bard employs NLP and machine learning to emulate human interactions, sourcing  responses from the Internet. Bard excels at crafting content tailored to specific audiences, making it invaluable for content marketing, ad copy writing, and more. It also refines content generation based on feedback, ensuring that the output remains relevant and engaging. Plus, it can be seamlessly integrated with various content management systems to streamline specific content creation and distribution processes.

PaLM 2 

Google’s latest LLM, PaLM 2, is set to become a key rival of GPT-4. While this model is not as widely recognized, PaLM 2 has made contributions to the field with its unique approach to processing and analyzing language data. With strong logic and reasoning, thanks to its broad training, it’s being applied to power various features and products, including the aforementioned Bard. 

T5 (Text-to-Text Transfer Transformer) 

Google’s T5 has showcased impressive performance across a wide range of NLP tasks. Its noteworthy contribution lies in its ability to handle diverse tasks such as language translation, text summarization, and question answering with remarkable accuracy.

LaMDA (Language Model for Dialogue Applications)

Developed by DeepMind, LaMDA can engage in conversation on any topic while providing coherent, in-context responses. You’d almost think you were chatting with a knowledgeable aunt or uncle. 

Turing NLG 

One of Microsoft’s contributions, Turing NLG stands out for its large-scale and efficient performance in language-generation tasks.

Open-source LLM examples

There are also some prominent open-source models, including:

• BLOOM (BigScience Large Open-science Open-access Multilingual Language Model), a multilingual LLM created mainly for text generation by Hugging Face.
• Claude 2: Anthropic’s latest competitive challenge to the GPTs.
• GPT-J, a powerful model known in the AI community for impressive language-generation capabilities. Its accessibility makes it an attractive option for researchers, developers, and enthusiasts alike.
• Llama (Large Language Model Meta AI): a multiversion LLM with performance similar to GPT-3.
• RoBERTa (A Robustly Optimized BERT Pretraining Approach): This variant of BERT addresses limitations of its predecessor and has achieved state-of-the-art performance on various language tasks. It’s advancing the accuracy and robustness of language models, boosting reliability and applicability.

That does it for our current list of large language models. As you can see, the race to dominate this field is off to a strong start.

Meanwhile, LLM technology will continue to revolutionize the many language-based realms it comes into contact with, including enterprise search.

How can LLMs improve search? 

Large language models make search results more accurate, too. That’s a salient consideration for ecommerce platforms storing large volumes of data and still tapping only traditional search algorithms, as the search results produced may not be especially on target. 

With this in mind, Algolia has integrated cutting-edge AI technologies made possible through vector embeddings and neural networks to boost the power of ecommerce search for sites ranging from startups to established contenders. NeuralSearch supplies results that are not only accurate but contextually relevant and personalized. Whether an English-speaking searcher wants a “black formal gown” or a “cute summer sundress”, it recognizes the nuances and delivers results that closely align with the shopper’s expectations.

To grow your ecommerce site’s bottom line, are you ready to improve your customers’ search results? Contact us or check out a demo to learn how our API could immensely strengthen your business.