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Large Language Models vs Generative AI: The Differences You Should Know About

author

Trinh Nguyen

Jun 21, 2024

Page Content
Key Differences Between Large Language Models and Generative AI
1. Overview
2. Generative Capabilities
3. Core Technologies
4. Model Architecture
5. Model Training
6. Application Areas
7. Ethical and Practical Challenges
How to Choose Between Large Language Models and Generative AI?
Collaboration Between Generative AI and LLMs
Final Thoughts

Generative AI vs. LLM, What is the big difference?

Artificial intelligence (AI) has made significant strides, particularly following the breakthrough of ChatGPT. Generative AI (GenAI) and large language models (LLMs) emerged as two pivotal advancements.

While both are designed to produce content, they differ considerably in their approaches, applications, and more.

This article aims to clarify all the key differences between large language models and generative AI that you should know about to make a wise decision for your business’s AI adoption strategy.

So, let jump in!

Key Differences Between Large Language Models and Generative AI

To sum up, here’s a brief summary of key differences between LLMs and generative AI:
brief summary of key differences between LLMs and generative AI

1. Overview

Generative AI refers to technologies that create new and unique outputs like images, videos, music, and text from learned data. These systems rely on advanced machine learning (ML) models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). By using large datasets, generative AI can produce creative and innovative results in many different forms.

Some well-known generative AI tools today include DALL-E from OpenAI, Midjourney, and Claude from Anthropic.

On the other hand, large language models are a specific type of generative AI focusing on producing human-like text. Examples of LLMs include OpenAI’s GPT and Google’s Gemini.

These models utilize machine learning frameworks called transformers, which employ a special mechanism known as self-attention.

Essentially, self-attention allows models to determine the importance of different words relative to each other in a text. It achieves this by analyzing each word (or part of a word) and figuring out which ones are most important for understanding the overall meaning. This ability thus helps LLMs create coherent and contextually accurate text.

2. Generative Capabilities

Both generative AI and LLMs can generate new content, but their specific capabilities vary significantly.

Here are some of LLMs’ key generative prowess:

  • Text generation: Produce coherent and context-aware text from user input, ranging from content marketing materials and fiction to code.
  • Translation: Translate text from one language to another. However, oftentimes, they don’t perform as well as specialized translation models, particularly with less common languages.
  • Question answering: Be able to provide explanations by simplifying complex terms or using analogies. They are also capable of offering advice and answering natural language questions, though their accuracy with factual answers is sometimes limited.
  • Text summarization: Be able to distill lengthy passages into concise key points. For instance, Google’s Gemini 1.5 Pro can analyze up to a million tokens at once, equivalent to about 750,000 words or nine average-length novels.
  • Dialogue: Engage in back-and-forth conversations, making them suitable for use in chatbots and virtual assistants.

Meanwhile, generative AI encompasses a wider range of applications beyond just text.

  • Image generation: Midjourney and DALL-E tools help create images from textual prompts. Adobe Firefly also enables users to edit parts of existing images, like changing the background of a portrait.
  • Video generation: Emerging models, such as Sora from OpenAI, excel in producing both realistic and animated video clips based on user descriptions.
  • Audio generation: GenAI models are also capable of creating music, speech, and other audio types. For example, Eleven Labs’ voice generator converts text data into spoken audio, while Google’s Lyria generates instrumental and vocal music.
  • Data synthesis: Generative models can produce artificial data that imitate real-world data, which is useful for training machine learning models when actual data is scarce or sensitive. For instance, a medical team can use synthetic data to reduce reliance on personal health information during model training.

3. Core Technologies

Large language models rely on transformer models, which use a mechanism called self-attention to evaluate the importance of different parts of the text relative to each other. This makes LLMs highly effective for tasks that require a deep understanding of language and context.

In contrast, generative AI employs GANs and VAEs technologies. These models create new outputs by imitating the patterns and structures found in the input data. By learning these distributions, they can generate unique and creative content accordingly.

4. Model Architecture

The architecture of LLMs also differs from that of other generative AI models in several ways.

Most modern LLMs are built using transformers. The attention mechanisms in transformers help them understand long text passages by modeling the relationships among words and determining their relative importance. Interestingly, transformers are not exclusive to LLMs; they can also be used in other types of generative AI models, such as those for image generation.

However, certain model architectures used in non-language generative AI are not found in LLMs. Convolutional neural networks (CNNs) are a case in point. They’re mainly used in image processing and specialize in analyzing images to identify features, from simple edges and textures to complex objects and scenes.

5. Model Training

The training of LLMs and GenAI models differs as well.

Large language models are specifically trained on extensive datasets composed predominantly of text, such as novels, news articles, and online posts. In contrast, generative AI models utilize diverse data types, depending on their intended applications, like images, audio files, and videos.

This variation in data types leads to different training processes. For instance, the data preparation for a large language model involves text-specific preprocessing and normalization techniques, while an image generator would require image-specific methods.

6. Application Areas

LLMs excel at extensive text interaction areas. They are commonly used in customer support systems and educational tools, providing assistance through natural language processing. Also, in the finance industry, LLMs help with tasks like fraud detection by analyzing textual data to identify irregularities.

Meanwhile, generative AI has much more wide-ranging applications across numerous industries, including the creative arts, science, finance, and beyond.

Read more: Leverage Large Language Models for Custom Projects

7. Ethical and Practical Challenges

Due to their reliance on vast datasets, both LLMs and generative AI face ethical and practical challenges related to data biases and copyright issues.

LLMs have drawn criticism for potentially facilitating academic dishonesty and the dissemination of misinformation due to their capacity to generate convincingly human-like text.

Whereas, generative AI, with its ability to create synthetic content, raises concerns about the proliferation of deepfakes and other manipulated media.

How to Choose Between Large Language Models and Generative AI?

How to Choose Between Large Language Models and Generative AI?

When deciding between generative AI and large language models, you should pay close attention to several key factors as follows:

  • Type of content

Generative AI has a knack for creating diverse content, such as images, music, and code, in addition to text. On the other hand, LLMs are specialized for tasks involving text only, such as natural language understanding, text generation, language translation, and textual analysis.

  • Data availability

Generative AI requires huge datasets that match the specific type of content being generated. LLMs, however, are optimized to work with vast textual data and perform best in scenarios where text is abundant.

  • Task complexity

Generative AI is often preferable for complex and creative content generation or tasks needing diverse outputs. LLMs, with their focus on language understanding and text coherence, are better suited for tasks demanding precise and contextually accurate text-based responses.

  • Computational resources

Larger generative AI models need more computational power and storage, while LLMs, focusing on text-centric tasks, can be more resource-efficient.

  • Training data quality

Generative AI needs high-quality and diverse training data to create meaningful and innovative outputs. For LLMs, having large and clean text corpora is enough to have effective language understanding and generation.

  • Development expertise

Developing and fine-tuning generative AI models often demands advanced expertise in machine learning and domain-specific knowledge. LLMs, especially pre-trained models, are more accessible and user-friendly for text-based tasks and require less specialized expertise.

Ultimately, your choice between generative AI and LLMs should align with your project objectives, the nature of your content, and the resources available. In some cases, leveraging both generative AI and LLMs together can provide a comprehensive solution to address various aspects of a project.

Learn more: How to choose between GenAI and AI

Collaboration Between Generative AI and LLMs

Collaboration Between Generative AI and LLMs

When used together, generative AI and large language models can significantly enhance various applications and unlock exciting possibilities.

Content Generation

Combining LLMs and generative AI models allows for the production of original, contextually relevant content across multiple domains, including images, music, and text. For example, a generative AI model trained on a dataset of paintings can be enhanced by an LLM that understands art history and generates descriptions and analyses of artwork.

This combination is particularly beneficial for eCommerce, as it enables the creation of compelling marketing images and persuasive text that can better engage shoppers. Whether used on social media or an online store, AI-aided content helps quickly win over customers and increase sales.

Content Personalization

By utilizing both generative AI and LLMs, you can significantly personalize content for individual shoppers.

LLMs analyze shopper preferences and generate personalized recommendations, while generative AI systems create customized content based on these preferences. This collaboration results in targeted product suggestions, personalized content, and ads for items of interest, thereby enhancing the shopping experience.

Chatbots and Virtual Assistants

The combination of LLMs and generative AI can also improve the conversational abilities of chatbots and virtual assistants.

LLMs provide context and memory capabilities, while generative AI produces engaging responses. This leads to more natural, human-like interactions, ultimately boosting shopper satisfaction and support experiences.

Multimodal Content Generation

Combining large language models with other generative AI models that handle modalities like images or audio allows for the creation of multimodal content.

For instance, a generative AI system can generate text descriptions for images or create soundtracks for videos. By blending language understanding with image/audio generation, these AI systems can produce richer, more immersive content that captures the audience’s attention.

Language Translation and Localization

When paired together, LLMs and GenAI models significantly improve content translation and localization.

This is because LLMs do a good job of understanding language nuances, and generative AI adeptly produces accurate translations and localized versions of content. This facilitates real-time, precise translations that are contextually appropriate, improving global communication and content accessibility.

Final Thoughts

Large language models and generative AI represent great advancements in artificial intelligence, each with unique capabilities and applications. Understanding their different roles and capabilities is thus crucial for navigating them effectively and responsibly.

The integration of these two technologies across sectors holds transformative potential, yet it also presents substantial ethical challenges and risks.

That’s when your business needs a Generative AI development & consulting partner to fight against all the odds!

At Neurond, we drive ethical and responsible innovation in generative AI development, leveraging our deep expertise to deliver tailored solutions worldwide. Our team harnesses cutting-edge LLMs and generative models like GPT-3.5, GPT-4.0, Claude, and Gemini, ensuring rapid, secure, and highly accurate responses that keep your organization ahead of the curve.

So, if you’re an enterprise seeking to harness advanced AI technologies such as generative AI and LLMs, contact us today to discover how our solutions can benefit your business!