The difference between LLM and a Foundational Model

A Large Language Model (LLM) and a Foundational model are related but distinct concepts in the field of natural language processing. The main difference lies in their specialization and use cases. A foundational model is a general-purpose language model, while an LLM is a language model fine-tuned for specific conversational applications. The LLC builds upon the foundational model to provide a more context-aware and coherent conversation experience.

To put it plainly: Think of Foundational Models as very smart robots that know many words but can’t talk like people. LLMs are special robots that are trained to talk like humans. They are better at having conversations and answering questions, kind of like a friendly robot friend you can talk.

Another analogy would be something like this: Imagine a foundational model like a big library full of words and books. It knows a lot about words but doesn’t talk like people do. Now, think of an LLM, as a special book in that library which knows how to talk like people, and it’s good at having conversations. It’s like a talking book that can answer questions and chat with you.

Foundational Model:

A foundational model, often referred to as a “base model” or a “pre-trained model,” is the core architecture that serves as the basis for more specialized models. These foundational models are trained on massive amounts of text data and learn to understand and generate human-like text.

They are typically large in size and have a vast vocabulary. They have a general understanding of language but require further training to be tailored to specific tasks.

There are many other foundational models available, both proprietary and open-source. Examples of foundational models include GPT-3, BERT, PaLM and LLaMa to name a few. These models are designed to handle a wide range of natural language processing tasks and can be fine-tuned for specific applications.

Large Language Model (LLM)

An LLM, such as ChatGPT, is an instance or a specific use case of a foundational model. It is a large language model based on a foundational architecture but is fine-tuned and specialized for generating human-like text in conversational interactions.

LLMs like ChatGPT are designed for chatbots, virtual assistants, or text-based dialog systems. They have undergone additional training to make them more coherent, context-aware, and suitable for natural language conversations.

LLM have been fine-tuned on a wide range of dialog data, making them better at maintaining context and generating appropriate responses in conversations.

What is the difference between an LLM and ChatGPT?

An LLM (Large Language Model) is a general term for a big language model like ChatGPT, which is a specific example of an LLM.

ChatGPT is a type of LLM that is trained and fine-tuned specifically for chat and conversation. It’s designed to be good at having text-based conversations, like answering questions and chatting with people. So, ChatGPT is a more specialized version of a general LLM, tailored for conversation. ChatGPT is a LLM model that is fine-tuned through reinforcement learning, specifically reinforcement learning from human feedback (RLHF)

There are different versions of ChatGPT tailored for specific tasks or languages. For example:

ChatGPT for Specific Languages: There can be versions of ChatGPT designed to work well in particular languages, like ChatGPT in French, Spanish, or other languages. These models are fine-tuned to understand and generate text in those specific languages.
Industry-Specific ChatGPT: Some ChatGPT variations are trained and fine-tuned for specific industries or domains. For instance, there might be a ChatGPT model specialized for healthcare, finance, or customer support, which understands the language and context specific to those fields.
Customized ChatGPT: Organizations or developers can also create their own versions of ChatGPT by fine-tuning the base model for their unique needs. This allows them to have a chatbot or virtual assistant that’s tailored to their specific requirements.

So, while “ChatGPT” is a term commonly used to refer to OpenAI’s conversational AI models, there can be various versions and variations of ChatGPT designed to suit different languages, industries, and use cases.

GPT-4, for instance, a giant in the field with a staggering 170 trillion parameters, trained on more than 5 trillion words.

Are there other LLMs?

Yes, there are many other LLM models available. Some of the popular ones include:

PaLM / PaLM2: Google’s Pathways Language Model (PaLM) is a transformer language model that can perform common-sense and arithmetic reasoning, joke explanation, code generation, and translation
BERT: The Bidirectional Encoder Representations from Transformers (BERT) language model was also developed at Google. It is a transformer-based model that can understand natural language and answer questions.
LLaMA (Large Language Model Meta AI) developed by Meta AI is a foundational model with 65 billion parameters 1. LLaMA is designed to help researchers advance their work in the field of AI.

There are many other LLM models available, both proprietary and open-source.

Fine-tuning Foundational Models

Fine-tuning an LLM refers to the process of retraining a pre-trained language model on a specific task or dataset to adapt it for a particular application. It allows us to harness the power of pre-trained language models for our exact needs without needing to train a model from scratch.

Fine-tuning involves taking a pre-trained foundation model and then training it further on a new dataset that is related to the original dataset. This can help the foundation model perform better on the new task. To fine-tune a foundational model for your own project, you can use techniques like fine-tuning and prompt engineering.

Fine-tuning entails providing the model with task-specific data tailored to a business’s unique use case. This process guides the model to focus on patterns and knowledge relevant to the task, a form of “transfer learning” in machine learning.

Continuous evaluation of the model’s performance on validation data and adjustments to hyperparameters ensure effective learning. It’s an iterative process: teams fine-tune, evaluate, and refine until performance goals are met.

Where can I find more Foundational Models and LLMs?

As of this moment in time, Hugging Face is the most popular open-source library for natural language processing (NLP) and LLMs that provides access to many state-of-the-art models. It lists over 350,000 models in its library.

GitHub also has a fair list of LLM and foundational models listed, but

Google Research: Google Research has released several open-source models, including BERT and T5 1.

Microsoft: Microsoft has released several open-source models, including the DeBERTa and UniLM models 1.

GitHub: GitHub is a great place to find open-source foundational models. You can search for models by keyword or browse through repositories that contain models