Large Language Model: The Subset of Foundation Models?

We live in a century where the majority of things are technology-driven. From automating a minor task to carrying out the whole data extraction process, AI is needed at each stage. In recent times, LLM and foundation Models combined have been the driving force behind the AI revolution worldwide.

While both of these terms have been in the limelight for quite some time now, the confusion among them prevails as well. While some might consider LLM as the successor, others think of foundation models as the successor. But something that we as users need to notice is that they are rather complementary than substitutes.

This blog will help form a clear idea of what large language models and foundation models are and how each model works. We will also analyze whether large language models are a subset of foundation models.

Know more about foundation models

Foundation models are the transformative shift behind the AI uprising with their ability to learn from vast datasets and perform multiple tasks as their key characteristics. Follow along to learn more about the foundation models and their workings.

What is a foundation model?

The foundation model, also known as general-purpose artificial intelligence (GPAI), is trained on massive datasets. These models employ self-supervised techniques and often contain billions of parameters. The adaptability of these models is what makes them stand out in the technology and advancement arena.

Foundation models can perform tasks like natural language processing (NLP), along with answering questions and image classification. These unique qualities and abilities are why these models are used as base models for developing more specialized downstream applications.

Some of the examples of foundation models include:

• BERT (one of the first foundation models)

• AI21 Jurrasic

• Amazon Titan 

• GPT

Features of foundation models

Several key features of foundation models make it a hit and user-favourite in a matter of time. The key features that set foundation models apart are mentioned below.

• Foundation models are trained on large-scale datasets, enabling them to learn from diverse sources.
• Trained on large datasets, reducing extensive manual annotation needs.
• The multimodal capabilities for performing multiple tasks like image generation and language translation.
• Lastly, these models employ self-supervised learning techniques for developing a foundational understanding of language and visual content.

These were some of the major features of foundation models that lure businesses and general users in.

Applications of foundation models 

The foundation models are well known for the wide variety of applications offered to the users. Several sectors, like healthcare, finance, and media, are known for leveraging foundation models for various purposes. Some of the applications of foundation models are mentioned below.

• Natural Language Processing for tasks such as translation, content generalization, and summarization.

• It can be used for image generation based on textual description.

• Customer support can be provided via automated chatbots.

• These models can be used to analyze medical images and carry out diagnostics.

Know more about the Large Language Model (LLM)

Large Language Models (LLMs) signify a major breakthrough in artificial intelligence (AI), particularly in the realm of Natural Language Processing (NLP). As LLMs continue to advance, they hold the promise of even greater progress. Keep reading to explore LLM, its definition, functionality, and some of its key applications.

What is a large language model (LLM)?

Large Language Model, also referred to as ‘Neural Networks’, is a type of AI program that can recognize and generate texts. These models are built on machine learning, specifically a type of neural network: transformer architecture. These transformer models can learn context and use a mathematical technique called ‘self-attention’.

The LLM employs a type of machine learning called ‘deep learning’ to understand characters, words, and sentences. Deep learning is one of the methods of AI, which teaches computers to process data in a way similar to the human brain.

Some of the examples of LLM include:

• PaLM by Google Pathway Language Model

• GPT Series by OpenAI

• BERT (Bidirectional Encoder Representations from Transformers)

• Llama released by Meta

Features of large language model (LLM)

There are several features of LLM that not only boost the working of these models but also leverage the self-attention technique. Some of the key features are mentioned below.

• The LLM can perform even those tasks they are not trained for via zero-shot and few-shot learning.
• These models provide versatility in applications ranging from text generation to sentiment analysis.
• They contain billions of parameters, which are adjustable while training.
• These models can be fine-tuned and customized for specific tasks and domains.

Applications of LLM

One of the key features of large language models is the versatility of applications it provides to the users. Several sectors, like education, finance, and legal, have swayed towards and benefitted from these applications. Some of the key applications are mentioned below.

• LLM as Generative AI for generating texts in reply to any prompt given.
• These models can be used for performing sentiment analysis.
• The LLM can be used to power AI-driven chatbots for customer handling.
• Furthermore, these models can be used for content generation.

Relationship between LLM and Foundation Models 

LLMs are large language models that are trained on massive datasets specifically to carry out language-related operations like recognizing and generating texts. Whereas the foundation models are trained on large-scale datasets to carry out a bunch of operations ranging from NLP to question answering.

Furthermore, it can be said that whatever functions a functional model and an LLm provide are similar, with the LLM focusing specifically on the language aspect of the data and the foundation model focusing on several aspects, including language. Thus, it can be concluded that the large language models are a subset of foundation models.

This Venn diagram can be used to understand the workings of machine learning and how deep learning techniques are a subset of ML. Furthermore, foundation models are a subset of deep learning, and large language models are a subset of foundation models.

NOTE: All LLMs are considered to be Foundation models but not all foundation models are LLMs. LLMs focus specifically on language tasks, while foundation models focus on a wider range of functionalities across different data types.

Final Verdict

Large language models and foundation models share several similarities like both types of models employ the same training methodologies. Moreover, foundation models provide the much-needed base on which large language models are developed to be fine-tuned for specific applications. While foundation models focus on a wide array of applications and functionalities, LLM is focused on one particular segment of those functionalities.

Thus, with several features and functionalities, it can be concluded that both models are codependent on each other instead of substituting one another. With the rapid advancements in the field of AI, this interplay continues to grow more and more crucial to driving innovations along with enhancing our ability to interact with machines. Ultimately, revolutionizing how we interact with and process information in this rapidly evolving digitalized world.