Large Language Models v/s Generative AI: Let’s understand the difference

If you want to know the basic difference between large language models v/s Generative AI, you have landed in the right place.?

Generative AI, in a general sense, refers to technologies that can generate various new outputs like photos, songs, and synthetic data. For instance, a PC could make art or act as if it were a complex medical condition.

Within the family of generative AI, Large Language Models (LLMs) excel at generating text that closely resembles human writing. They acquire knowledge from vast amounts of text to create anything from emails to long reports.

While both Generative AI and LLMs are anchored on AI principles, their roles, uses, and repercussions differ substantially. Let’s reveal brief information on these buzzworthy technologies.

What is Generative AI?

Generative AI is a technology category, which can produce new outputs, including images, videos, music, and text, from learned data.

These models use more sophisticated forms of machine learning, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

Creative and innovative outcomes in different formats are powered by using big datasets.

Some of the popular generative AI tools today include DALL-E by OpenAI, Midjourney, and Claude by Anthropic.

Generative AI Applications

The scope and nature of generative AI applications is vast.

In the arts, generative AI can be used to create or supplement artwork and music compositions, bringing new perspectives and expanding creative boundaries.

One such recent study in genetics exemplifies this breakthrough. A generative AI system that connects with CRISPR technology has made it possible to invent new gene editors.

Generative AI helps financial services analyze market trends, automates document standardization in law, and empowers marketers to create strategic content.

Challenges of generative AI

One of the main concerns about generative AI is the ethical implications of deep fake technologies. Deepfakes are videos and images made so well that they mimic real people without consent.

An employee from a high school was arrested recently after creating a deep fake audio clip to defame the principal.

Copyright concerns exist as AI-generated content blurs distinctions between original works and derivative creations.

Finally, generative AI technologies continuing to evolve poses a risk of job losses across multiple sectors.

What are Large Language Models?

Large Language Models (LLMs) are a type of generative AI that can output text similar to the one generated by humans.

These models, for example Google’s BERT or OpenAI’s GPT, rely on transformer architectures in machine learning frameworks.

Transformers use self-attention to weigh words’ importance to each other within the model.

Self-attention aims at every word—or part of a word—in a text. Hence, it becomes possible to find out which words matter most when understanding the whole context.

Thus, the objective function is an important factor in LLMs’ learning process. For many LLMs, the objective function involves predicting the next sentence from prior ones. This is how they learn about language patterns and structure.

LLMs are trained using a variety of texts from different sources, such as books, Wikipedia articles, and Reddit posts.

This training allows them to mimic writing styles, answer questions and more generally, produce contextually appropriate text across various applications.

Large Language Models Applications

LLMs possess a wide array of applicability.

Within customer service, LLMs can automate interactions in the brand’s voice to handle everything from routine inquiries to more complex issues.

For instance, Freshworks and Zendesk use AI-driven elements like chatbots in their customer support services which address customer queries and take difficult issues to real-time agents. This is one way that LLMs and humans can combine efforts for better outcomes.

In the making of content, writers are supported by LLMs when it comes to such things as generating draft articles, suggesting edits or even producing new pieces of work based on given guidelines.

This is also true concerning fraud detection in finance where LLMs are employed. J.P. Morgan, for example, uses LLMs for payment validation screening.

Teachers may employ LLMs in education to help make lesson plans, grade assignments or create tailored individual plans for students.

Challenges of Large Language Models

LLMs are associated with a number of issues.

Just like other broad AI generative technologies, LLMs have serious implications for certain industries such as finance, journalism, and customer support.

In education and academia, LLMs can make it easy for people to cheat on their assignments. According to Nature , many articles were published in journals using the phrase “regenerate response” – which means that the LLM’s text was copied from ChatGPT or any other similar models.

Another significant challenge is data bias because these LLMs usually replicate and magnify biases present in their training sets. A study by Apple's Machine Learning Research investigating four different LLMs showed how these models tend to stereotype professions according to gender differences.

One major legal hurdle relates to copyright infringement concerns. Generally speaking, LLMs need lots of data during training which they often get by scraping content from various sources including copyrighted materials with no explicit permission required.?

Recently, OpenAI, together with Microsoft were sued for copyright infringement by the New York Times and a number of US newspapers illustrating the complex ethical and legal landscape around LLMs

Final Thoughts

As a whole, generative AI goes beyond the limits of creative content production but LLMs on the other hand is mainly aimed at enhancing how we generate and interact with text-based data.

The incorporation of these technologies into different sectors has transformative power, although it raises considerable ethical concerns and jeopardy.

By comprehending the exact functions as well as capacities in the wider spectrum of generative AI, we can more easily steer through them and use them properly.