AI Basics series : All LLMs are FMs, but not all FMs are LLMs

All LLMs are foundation models, but not all foundation models are LLMs. 


Let's break it down to clarify:

1. All LLMs are foundation models:

  • Foundation Models (FMs): These are large, pre-trained models trained on vast amounts of unlabelled data at scale. They are designed to be "foundational" because they can be adapted (fine-tuned) for a wide range of downstream tasks without needing to be trained from scratch for each specific application. Their pre-training allows them to learn general representations and patterns across the data.

  • Large Language Models (LLMs): These are a specific type of foundation model that is specialized in processing and generating human language. They are trained on massive text datasets and excel at tasks like text generation, translation, summarization, and question answering.

Since LLMs fit the definition of a foundation model (large, pre-trained on vast data, adaptable), every LLM is inherently a foundation model.

2. Not all foundation models are LLMs:

  • As mentioned, foundation models are a broader category. While LLMs focus on language, foundation models can be trained on other types of data (modalities) as well.

  • Examples of non-LLM foundation models:

    • Vision Foundation Models: Models trained on vast image and video datasets (e.g., CLIP, DALL-E's latent space models). They can be adapted for tasks like image classification, object detection, image generation, etc.

    • Multimodal Foundation Models: Models that learn from and process multiple modalities simultaneously (e.g., text and images, or text, images, and audio). These can understand the relationships between different data types.

    • Speech Foundation Models: Models trained on large audio datasets for tasks like speech recognition, speech synthesis, and speaker identification.

    • Code Foundation Models: Models specifically trained on large code repositories, capable of code generation, completion, and bug fixing.

In summary:

Think of it like this:

  • Foundation Model is the broad category (e.g., "Vehicles").

  • LLM is a specific type within that category (e.g., "Cars").

All cars are vehicles, but not all vehicles are cars (there are trucks, motorcycles, buses, etc.). Similarly, all LLMs are foundation models, but not all foundation models are LLMs because there are foundation models for vision, speech, code, and other modalities.

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