As you explore ways to enhance your product with AI, you may need clarification about the distinctions between large language models and generative AI. You’re not alone. Many feel overwhelmed by the similarities between these two technologies, which can lead to ill-informed decisions when integrating them into products. This article breaks down the core differences between multimodal LLM and generative AI so you can make informed choices for your AI tech stack.
Lamatic’s generative AI tech stack can help you achieve your goals. Our solution empowers you to seamlessly distinguish between LLMs and generative AI so you can focus on integrating the technology that best suits your product’s needs.
Is ChatGPT Generative AI or LLM?
ChatGPT is an AI chatbot that can answer your questions using data from the internet. Though that’s the simplistic answer, there’s also a more complex one. ChatGPT is powered by language models created by OpenAI known as generative pre-trained transformers, or GPTs.
This kind of AI can generate new content instead of just analyzing data. If you’ve heard of large language models or LLMs, a GPT is a type of LLM. Got it? Good.
How Does ChatGPT Work?
How does ChatGPT generate human-like text? ChatGPT runs on GPTs, which OpenAI regularly updates with new versions, the most recent being GPT-4o. Trained by humans and a ton of internet data, each model can generate human-like conversations so you can complete all kinds of tasks.
What Can ChatGPT Do?
The possibilities are endless, from composing essays and writing code to analyzing data, solving math problems, playing games, providing customer support, planning trips, helping you prepare for job interviews, and so much more.
Here’s just a short list of what it’s capable of:
- Passing MBA exams
- Being your girlfriend
- Writing uncreative TV scripts
- Helping with medical diagnoses
- Explaining complex scientific concepts
- Drafting college essays
- Advertising Coke (the soda)
Is ChatGPT Dangerous?
For all its hype, at its current level, ChatGPT, like other generative AI chatbots, is a dim-witted computer that sits on a throne of lies. For one thing, it hallucinates.
What is AI Hallucination?
It's not that kind of hallucination. In AI, hallucination refers to an AI-generated process in which the tool tries to extrapolate and create from collected data. It gets it absurdly wrong, creating a new reality.
What are the Risks of ChatGPT Hallucinations?
It doesn’t bear a resemblance to actual human hallucinations, and I think it makes light of mental health issues, but that’s another subject. In other words, does ChatGPT sometimes generate incorrect information? Incorrect information is a weak way of putting it. ChatGPT fabricates facts altogether, which can lead to the spread of misinformation with serious consequences. It’s made up of:
- News stories
- Academic papers
- Books
Lawyers using it for case research have gotten in trouble when it cited nonexistent laws. Sometimes, it gives the middle finger to reality and human language and spouts pure gibberish. Earlier this year, for example, a malfunctioning ChatGPT that was asked for a Jackson family biography started saying stuff like,
“Schwittendly, the sparkle of tourmar on the crest has as much to do with the golver of the ‘moon paths’ as it shifts from follow.”
Which is probably the worst description of Michael Jackson’s family in the world.
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LLM vs Generative AI Insights for Smarter Integration
Generative AI is a broad category of artificial intelligence focused on creating new content:
- Text
- Images
- Audio
- Video
Unlike traditional AI, which is often rule-based or deterministic, generative AI leverages probabilistic models to create original outputs based on learned data patterns.
What are Large Language Models (LLMs)?
Large language models are specialized generative AI focusing on processing and generating human-like text. Built primarily on advanced transformer architectures, LLMs are trained on massive amounts of text data to predict and generate natural language responses, understand context, and answer questions coherently. However, LLMs are tailored to text alone rather than multimedia content.
Generative AI vs. LLMs: What Are the Key Differences?
Generative AI versus large language models. LLMs and generative AI have become everyday terms in tech and business circles. Though they’re often used interchangeably, they serve different purposes and are incomparable in functionalities. Let’s ask ChatGPT to give an analogy representing these two terms’ differences.
- Generative AI: It is like a chef who knows tons of recipes. If you ask this chef to create a new dish with your favorite ingredients, they’ll mix things up and make something unique just for you.
- LLM: Is like a well-read recipe librarian. This librarian has read thousands of cookbooks. If you ask about a recipe, they’ll quickly give you a summary based on what they’ve read.
In short:
- Generative AI: Like a chef inventing a new dish from scratch.
- LLM: Like a knowledgeable librarian who can answer questions based on lots of reading.
Generative AI is a Broad Area of AI, and LLMs are One Form of Generative AI
Generative AI is a broad category of artificial intelligence systems designed to generate new content based on learned patterns from vast datasets. These systems quickly create various forms of media, including:
- Text
- Images
- Audio
- Even video
Generative AI's core work is to create something original from existing data, making it useful in fields like content creation, design, entertainment, and even scientific research.
Large language models, like GPT-3 and GPT-4, are specialized generative AI focusing on text generation. They are trained on massive text datasets and use this training to generate coherent and contextually relevant language responses, understand context, and answer questions. However, LLMs are tailored to text alone rather than multimedia content.
LLMs are Text-Only Outputs, While Generative AI Has Multimodal Abilities
LLMs were limited to processing and generating text. Early models like GPT-3 could only take text as input and produce text-based outputs. This limitation made LLMs incredibly powerful for various text-heavy tasks, such as chatbots, content generation, language translation, and question-answering.As the field evolved, multimodal models emerged, blurring the lines between traditional LLMs and broader generative AI tools. For example, OpenAI’s GPT-4, considered a multimodal model, can now accept text and image inputs, enabling it to generate text responses based on images or understand context from a combination of text and visuals. Similarly, multimodal generative AI models can process and create video or audio content, expanding their potential beyond text.
While LLMs still predominantly focus on language-based tasks, generative AI models encompass broader capabilities. These tools can generate:
- Images (e.g., DALL-E)
- Music (e.g., Jukedeck)
- Video (e.g., Runway ML)
- 3D models
The Expanding Role of Both LLMs and Generative AI
Both generative AI and LLMs have become part of many applications.
LLMs
LLMs have grown more powerful, with advancements such as GPT-4, which has a massive 175 billion parameters, leading to more accurate and sophisticated text generation. The increase in the number of parameters in these models enhances their ability to generate more coherent, contextually relevant, and nuanced text, bringing more lifelike conversations, better content, and improved problem-solving capabilities.
Generative AI
Generative AI tools are expanding in terms of model size and the types of tools available. Platforms like Midjourney, DALL-E, and Runway ML are examples of generative AI specifically designed for creative industries. These tools allow users to generate images, animations, and videos from simple text prompts, revolutionizing art, media, and advertising industries.
Companies like Google and Meta are developing generative AI systems that can produce more complex multimedia content, including:
- 3D models
- Videos
- Simulation
These are becoming increasingly useful in fields like product design, entertainment, and virtual reality.
Distinct Approaches to Model Architecture Generative AI vs. LLM
While generative AI and LLMs may use transformer models, their architectures and training processes often diverge to suit their intended outcomes.
Generative AI models
Besides transformers, generative AI uses architectures like GANs and VAEs. GANs consist of two competing networks (generator and discriminator) that "teach" each other to create more realistic images or sounds. VAEs compress data into a latent space to generate diverse outputs with slight variations, which is ideal for video or image synthesis.
LLMs
Built primarily on transformers, LLMs use self-attention mechanisms to learn linguistic context and semantics. These models, such as GPT-3, are trained on immense text corpora to anticipate the next word in a sequence, enabling them to generate coherent and contextually relevant text.
Challenges and Limitations: Where Generative AI vs. LLM Each Struggle
Each approach has unique challenges, with generative AI focused on cross-content consistency and LLMs dealing with factual accuracy and coherence.
Generative AI
Although generative AI is versatile and can have different abilities, it needs to work on delivering consistent quality across various content types. For example, generating realistic videos requires immense computational resources, and AI-generated art often needs more nuance of human-created works.
LLMs
LLMs struggle with context beyond the text. They may generate language that sounds plausible but lacks factual accuracy. They also require substantial data and resources, which can be a hurdle in applications needing real-time language comprehension.
Overlapping Roles: Can LLMs Be Considered Generative AI?
It’s tempting to consider LLMs as part of generative AI, as they generate outputs based on input data. The key difference lies in the scope and application. LLMs specialize in natural language processing, focusing solely on language, whereas generative AI covers a broader spectrum, generating content across multiple formats and modalities.For that, let’s overview their differences in a table format.
Future Directions: Where are Generative AI and LLMs Headed?
As AI advances, both generative AI and LLMs are set to make strides in different directions:
Generative AI
Expected to enhance multi-modal capabilities. Gen AI will allow seamless integration of text, video, and image generation within a single framework. This could transform industries like gaming, film, and even education, where diverse content creation is crucial.
LLMs
They will likely improve contextual accuracy, enabling them to handle domain-specific queries and provide more reliable information. Advancements in fine-tuning and prompt engineering are expected to reduce errors and improve real-time customer support and healthcare applications.
Choosing Between Generative AI and LLMs
Understanding the distinction between generative AI and LLMs allows businesses and tech professionals to make informed decisions based on specific needs. While generative AI offers versatility across content types, LLMs provide specialized strength in language tasks. Each has the potential to reshape how we interact with information and content. We can discover new possibilities in AI-powered innovation by aligning the right technology with the right purpose.
This unique guide illuminates the core differences and nuances that set Generative AI and LLMs apart, offering a fresh perspective for anyone exploring their potential applications.
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