AIFuture technologyContent creationLLMsAI-driven content creation: Future of Large Language Models (LLMs)
Sherlin Jannet

Sherlin Jannet

6 min read

Humans are afraid of what the future holds. A significant portion of this fear can be attributed to progressing technologies. Modern technology and specifically artificial intelligence is advancing faster than man can comprehend—resulting in the assumption of a worst-case scenario—will Artificial Intelligence replace people?

Douglas Adams, author of “The Hitchhiker’s Guide to the Galaxy” describes our reaction to technologies with a set of rules— one of them being: “Anything that gets invented after you’re thirty is against the natural order of things and the beginning of the end of civilisation as we know it until it’s been around for about ten years when it gradually turns out to be alright really”. The way I see it, Artificial Intelligence will not replace us but rather advance us to more strategic roles. To be more forward, people using AI will replace job roles with basic or menial tasks.

There’s no time like now to learn to leverage AI so that your business has a competitive advantage over others and will stand out in the industry. And we’re here to help you start-off. This article explains large language models—the AI used in content creation and unravels three significant features that LLM’s will be capable of in the future, to give you an insight into the future of content creation.

What are LLMs?

Imagine a robot that knows a lot of words and understands how they fit together to form a language. This robot has also read a lot of books on several different topics. It can now have a conversation with you about anything from the books or even answer questions on its topics. This robot is called a Large Language Model.

Large language models are trained on large amounts of data which they run through several parameters in the model to produce suitable and meaningful text in response to prompts. This is called Natural Language processing (NLP) by which computers can learn to interpret, imitate and interact with human beings in an unconventional manner. For example, chat-bots are a product of large language models.

Examples of LLMs

  1. LaMDA (Language Model for Dialogue Applications) - this model was designed to have a more natural and engaging conversation with users, It can discern various subtleties and engage in open-ended discussions.

  2. LLaMA (Large Language Model Meta AI) – the language model was developed by Facebook’s Meta and is similar to other LLMs. Compared to other models it requires fewer resources and less computing power. Access to this model is restricted and requires an application to be submitted.

  3. Bloom (BigScience Large Open-Science Open-Access Multilingual Language Model) – it is a multilingual LLM. It can generate text in 46 natural languages and 13 programming languages.

  4. GPT-3 (Generative Pre-trained Transformer 3) – this model is part of the GPT series developed by Open AI. It is trained on an extensive amount of data. GPT – 3 can be fine-tuned to perform several tasks such as translation, summarization, question-answering and text-completion.

  5. GPT-4 (Generative Pre-trained Transformer 4) – a predecessor of GPT-3—it was trained on a larger dataset and has 175 billion parameters, while GPT-3 has 17 billion parameters. Added to what the previous model does, GPT-4 can perform tasks such as reading image and audio inputs, translate text to other languages and solve math problems to name a few.

  6. GPT-5 (Generative Pre-trained Transformer 5) – the LLM is yet to be released but is predicted to be much advanced to GPT-4, with the ability to detect sarcasm and irony, expanded knowledge base and even rumours of AGI, which is when machines can think on their own, similar to human

Fact-checking

While generative AI is able to provide information on a wide range of topics—in the future—there will also be a need for it to be factually correct. Especially when applied to content research and research in general. AI could prove to be of great help in making advancements, given that it is factually correct. While the current LLM models have access to a large amount of pre-trained data which they can use to produce information, content creators are still required to be aware of its limitations and cross-check facts. Looking forward, LLMs will be able to fact-check their information, making it easier to do content research. This can be done through the model having access to external resources, factuality scores or enabling the model to provide citations and references for its answers, leading us to the source of the information. The possibility of this has been outlined in a recent article by Microsoft research.

Added to this, Google’s Deepmind has introduced Sparrow, an LLM that can support factual claims by providing evidence from sources. Other models like Facebook’s RAG and OpenAI’s WebGPT are examples of undergoing-research showing immense potential in this field.

Self-learning

If you ask chatgpt about the 2022 World Olympics, it will tell you that it has no information on this because of its last update being September 2021. As LLMs are pre-trained models they can answer questions only within the context of the data they are trained on. In order to deal with other tasks they need additional training data that is to be manually added by a person. The next generation of models will be capable of self-learning, that is, the models will be able to adapt to various tasks without the need of additional training data. This way LLMs can give information on new topics and concepts without the need for human-generated data, by accessing external sources to learn from its own predictions. And so, content creators won’t be limited to old concepts or topics anymore.

A new algorithm called SimPLE (Simple Pseudo-Label Editing) developed by the MIT research team gives promise to the concept of self-learning models. The SimPLE algorithm uses textual-entailment (categorising an ordered pair of sentences into one or three categories). This equips the machine with the understanding it needs to self-adapt and match different tasks.

Personalised

Imagine if you have to delete your instagram and start over again. Even thinking about it is uncomfortable, isn’t it? The reason for it is your self-curated instagram feed, which has been fed with algorithms trained on hours of scrolling, now serving you personalised content that you agree with. This is why personalisation is the most comfortable attribute your LLM can have. It is easier to work with a machine that understands what you’re looking for, remembers your preferences and gets better at serving you, with time. This can be achieved with profile augmentation, an approach that uses information from your profile to construct personalised prompts. Added to this, LLMs will have increased memory capacity and will remember previous conversations with the user.

GPT has already been used to create systems that generate personalised content based on user data. Similar to this, the LaMP Benchmark is a novel method by Google research which ideates how LLMs can be used to produce personalised outputs based on user information

Our conclusion

Although current LLMs have limitations and users are following certain best practices such as manual fact-checking, AI still aids in a wide range of tasks making it easier to produce content. To explore the current features of GPT you can check out our website, Exemplary AI.

Future LLMs will be able to fact check the information they provide, making content research faster. LLMs will also be personalised to match your personality and preference, benefiting content creators and niche businesses greatly and equipped to self-learn and self-adapt to different tasks. The precision and utility AI will have in the future is exciting to think about and now is the perfect time to get on the current AI trend to not only outperform your fellow creators but to also keep up with other AI-powered content out there.

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