Artificial Intelligence

Large Language Models – What you need to know

16th June 2023
Harry Fowle

We are continuing to hear more about OpenAI’s GPT-4 or Google’s PaLM2 generative AI Large Language Models are up to, but what are these models, what should be considered when building them, and where are they being used?

Whilst you often hear the term generative AI when speaking about ChatGPT or similar models, this is more of an umbrella term. To be more specific, these models are actually Large Language Models (LLMs), so what’s the deal?

What are Large Language Models?

LLMs are artificial intelligence systems which are designed to understand and generate human-like text based on the input they receive. These models are often trained on vast amounts of text data to learn the subtle patterns, relationships, and nuances of human language.

These models employ a deep learning technique called transformers, which allows them to process and generate text with remarkable fluency and coherence. They can understand and generate text in multiple languages, respond to prompts or questions, write essays, create conversational dialogue, and perform a wide range of language-oriented tasks.

LLMs models are typically pre-trained on massive datasets which often contain billions of sentences from a variety of different sources such as books, articles, websites, and more. During pre-training, the models learn to predict what comes next in a sentence, capturing grammar, context, and semantics.

Once pre-trained, the models can then be fine-tuned for specific tasks, such as translation, summarisations, query-answering, chatbot interactions, or even more advanced tasks like drug development or security applications. This process of fine-tuning involves narrowing the model’s training with task-specific datasets that might include specific labels, tasks, or objectives.

What can LLMs achieve?

LLMs are a highly versatile piece of technology which have a wide range of applications, here are some of the key use cases for them:

Content creation

Content creation is one of LLMs most prevalent capabilities being widely deployed anywhere from casual fun to corporate endeavours. Content creation includes:

  • Idea generation
  • Text writing (books, training courses, plans, etc.)
  • Copywriting (emails, website content, social media posts, etc.)
  • Code writing

This section also includes the other side of LLMs which can incorporate other formats such as:

  • Image generation (text-to-image)
  • Video generation (text-to-video)
  • Voice generation (text-to-voice)
  • Generative designs (3D design, augmented reality, etc.)

Content curation & analysis

Content curation and analysis once again works on a similar plain to content creation, being used by a wide array of consumers. This section covers rewriting and summarising, data extraction, and data analysis. Examples include:

  • Text summary
  • Video summary
  • Audio transcripts
  • Language translation
  • Information clustering/formatting
  • Information retrieval
  • Web searching & benchmarking
  • Q&As
  • Data analytics & forecasting
  • Visualising analytics
  • Sentiment/intent recognition

Task automation

In a more corporate mindset, LLMs are being widely deployed to automate a considerable amount of day-to-day mundane tasks. These applications include:

  • Chatbots & virtual assistants
  • Scheduling (meetings, tasks, events, etc.)
  • Text editing (spellcheck, paraphrasing, etc.)
  • Visuals editing (video cutting, image editing, etc.)
  • Voice editing/cloning
  • Data cleansing
  • Code auditing
  • Controlling robotics

Things to consider when building LLMs

However, whilst LLMs can be excellent tools when developed and deployed effectively, there are many key considerations that must be undertaken when creating an LLM for any application.

  • Data – The more data the model is exposed to the better, this refers to not just sheer volume, but also diversity. Doing so will ensure better performance in general but also on unforeseen cases.
  • Deployment – Different deployment options, such as Cloud-based services, on-premises, or Edge devices, can be considered and configured depending on the application.
  • Model architecture – Which model architecture you chose to employ is critical for LLM performance in different scenarios.
  • Pre-processing – Cleaning up and preparing datasets for the LLM to train from, which includes tasks such as stemming and lemmatisation.
  • Fine-tuning – In order to ensure the LLM is capable of doing the specific tasks you wish, it is typically necessary to train it on task-specific layers of datasets during the training process.
  • Evaluation – It is wise to carefully assess the model to ensure that it is performing tasks it was designed to complete effectively.
  • Explainability – Due to the complex nature of the models, there should be measures in place to understand and explain how the LLM came to the predictions and answers it did.
  • Privacy & security – LLMs can come into contact and directly deal with sensitive information therefore it is crucial to consider privacy and security measures both prior to and following deployment.
  • Bias & toxicity – Due to AI’s lacking ‘humanity,’ they can inadvertently perpetuate and amplify biases and toxicity. For example, they might include offensive language due to its presence in training data, or fail to represent minority groups.

Where are we seeing LLMs being deployed

So where exactly are we seeing LLMs being deployed in the field? Whilst some obvious answers are of course the widespread use of chatbots, virtual assistants, and data analysis, some other niche examples are also seeing growing use.

For example, within the technology sector, software developers are reporting 88% higher productivity when utilising an LLM generative AI code assistant. Meanwhile, in consumer markets, automated on-model fashion image generation yielded a 1.5x increase in retailer conversion rates. Within the entertainment sector, novel animated motions are achieving a ~97.2% motion sequence quality score on a single training session of natural movements. Biopharma is another lesser-known benefactor of the rise and implementation of LLMs, with the models able to identify a novel drug candidate for the treatment of Idiopathic Pulmonary Fibrosis in just 21 days, something that can take years using traditional methods. Even financial institutions are reaping the benefits of LLMs with synthetic GAN-enhanced training sets having been used on LLMs to give them a fraud detection rate of ~98%. These are all great – and often overlooked – utilisations of LLMs in the real world and are only scratching the surface of the possibilities.

Statistics courtesy of Technology Innovation Institute (TII).

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