FOUNDATION MODELS AND LLM COMPARISON

Foundation models and Large Language Models are used in the context of Generative AI(Gen AI). Although they share similarities in architecture and purpose, there are distinct differences between them. Let us compare them

Foundation models and Large Language Models are used in the context of Generative AI(Gen AI). Although they share similarities in architecture and purpose, there are distinct differences between them. Let us compare them

Foundation Models:

Foundation models are large scale pre-trained models which can be fine-tuned for variety of downstream tasks. They serve as a "foundation" because they provide a versatile basis that can be adapted or fine-tuned for different applications. They are trained on extensive datasets covering diversified data types like text, images, audio etc. This makes them adaptable across multiple modalities. They are intended to be fine-tuned for a wide range of tasks not just limited to language tasks.

Examples:

• CLIP (by OpenAI): A model trained on images and their captions, capable of linking language with visual data. CLIP can categorize images based on text prompts or provide relevant images for text inputs, functioning across text and image data.
• DALL-E: Also by OpenAI, DALL-E generates images from textual descriptions. It's designed to understand both visual and textual contexts, combining knowledge from multiple domains.
• Whisper (by OpenAI): A model for automatic speech recognition that can transcribe speech into text across multiple languages, bridging audio and text modalities

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Large Language Models:

Large Language Models(LLM) are a subset of foundation models. They are designed and trained to understand and generate human language. Their primary goals are natural language processing, text generation, summarization, question-answering, translation and many more.

Examples:

• GPT-3 and GPT-4 (by OpenAI): These models are trained on massive text data and designed for diverse language tasks. They can answer questions, create content, perform language translation, etc.
• BERT (by Google): A model pre-trained for NLP tasks, BERT is highly efficient for sentence-level tasks like sentiment analysis and question answering. It focuses on understanding language semantics and syntax.
• PaLM (by Google): Another large language model tailored to diverse NLP applications, including multi-lingual support and language generation tasks.

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Applications

• Foundation Models: Foundation models can be used as a base model for building specialized applications across various domains.

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• Visual question answering: Models like CLIP can answer questions about an image.
• Text-to-image generation: DALL-E can create images based on detailed text descriptions.
• Speech-to-text and multi-lingual transcription: Whisper can transcribe spoken language into text.

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Large Language Models: Primarily used for text generation. Examples are as below

• Customer service: Chatbots powered by LLMs like ChatGPT provide customer support.
• Content generation: LLMs can generate articles, summaries, and creative writing.
• Search engines: LLMs help improve search results by understanding user queries contextually.

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Summary of the comparison in the table below

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