In the rapidly evolving world of artificial intelligence (AI), two terms have recently gained significant popularity: LLM (Language Model) and Generative AI. Both technologies have already been a buzzword in every other technology company and various new start-ups are also riding on this trend. With so much news around Generative AI and LLM, it is no surprise that some people have started using it interchangeably and others are not clear if they are the same or different. In this article, we will do a comparison between LLM vs Generative AI where we will understand their definitions, differences, and similarities.
What is Generative AI
Generative AI refers to a broader category of AI models that can generate new, previously unseen content. This includes not just text, but also images, music, and even videos.
Key Features of Generative AI
- Diverse Content Generation: Capable of producing various types of content like text, images, artwork, music, and videos.
- Training: Depending on the type of content there are various training methodologies like GANs, Diffusion, Transformers, etc.
- High-quality Outputs: The latest Generative AI platforms like ChatGPT, Midjourney, Stable Diffusion, etc. are producing content that is becoming more and more human-like generated content.
Popular Generative AI Models
Some of the popular Generative AI models are –
- Dall-E, Dall-E 2, Dall-E 3 by OpenAi
- Stabile Diffusion
- MusicGen by Meta
- StyleGAN & StyleGAN2 by NVIDIA
- BigGAN by DeepMind
- MuseNet by OpenAI
- DeepDream by Google
Some of the popular practical use cases of Generative AI are as follows –
- Content Creation
- Creating Artwork
- Graphics Design
- Image Processing
- Image In-Painting & Out-Painting
What is LLM
LLM, or Large Language Model, is a type of machine learning model designed to understand and generate human language. These models are trained on vast amounts of text data, enabling them to predict the next word in a sentence, answer questions, or even write essays.
Key Features of LLM
- Training: The modern LLMs are trained with Transformers on a very huge text dataset.
- Versatility: They can be fine-tuned for specific tasks, such as translation, summarization, or chatbots.
- Contextual Understanding: LLMs can understand context, making them adept at tasks like conversation and content generation.
Some of the well-known LLMs are –
- GPT Family by OpenAI: GPT, GPT-2, GPT-3, GPT-4
- BERT, PaLM by Google
- LLaMA by Meta
(Since LLM is a type of Generative AI, all LLMs listed above are also applicable to Generative AI)
Some of the popular practical use cases of LLM are as follows –
- Customer Support Chatbots
- Personal Assistant Chatbots
- Content Writing
- Language translation and transcription
- Question & Answer System
(Since LLM is a type of Generative AI, all its applications listed above are also applicable to Generative AI)
LLM vs Generative AI: Differences
While LLMs are primarily focused on language and text, Generative AI encompasses a broader range of content generation, including images, music, and videos.
Modern LLMs are trained on vast text datasets mostly with Transformer models, while Generative AIs can use Transformers, GANs, and Diffusion, depending on the content type.
LLMs are often used for tasks like translation, chatbots, and content writing. In contrast, Generative AIs can be used for creating artwork, music, or even the controversial deepfakes.
LLM vs Generative AI: Similarities
LLM is a subset of the Generative AI family that is used for generating human-like text content.
Both LLM and other Generative AI models in general are rooted in Deep Learning which is an area of Machine learning specialized for Neural Networks.
Both LLM and most other Generative AI models in general can be fine-tuned or adapted for specific tasks or domains.
The below table summarizes all the points that we discussed in our comparison of LLM vs Generative AI
|LLM (Language Model)
|A machine learning model designed to understand and generate human language. It is a subset of Generative AI.
|A category of AI models that can generate new, previously unseen content, including text, images, and videos.
|Primarily focused on language and text.
|Broad range of content generation, including images, videos, and music.
|Latest LLMs are trained with Transformers
|Various training methodologies like GANs, Diffusion, Transformers, etc.
|Models can be fine-tuned to adapt to specific custom tasks.
|Most of the Generative AI models can be fine-tuned to adapt to specific custom tasks.
|It is rooted in Deep Learning which is an area of Machine Learning that uses Neural Networks.
|All Generative AI models are rooted in Deep Learning
|GPT, GPT-2, GPT-3, GPT-4 BERT, LLaMA, Claude, Falcon
|Dall-E, Dall-E 2, Dall-E 3, Stabile Diffusion, MusicGen, StyleGAN, StyleGAN2, BigGAN, MuseNet, DeepDream, CycleGAN, DCGAN
|Popular use cases are chatbots, content writing, paraphrasing, translation, transcription, coding, etc.
|Popular use cases are Content Creation, Artwork Graphics Design, Image Processing, Image In-Painting & Out-Painting, etc.