Have you been hearing a lot lately about the large language models backing ChatGPT, and other futuristic applications?
Do you wonder about the power these large language models are slowly gaining as they’re learning to predict and generate text? Are you having qualms about their long-term effect or the impact of teaching AIs human languages?
Well, you’re not alone. Let’s get started.
A large language model (LLM) is a type of machine learning model that can perform a wide range of natural language processing (NLP) and natural language generation (NLG) tasks.
These specialized models are able to do general things like answering questions in an easy-going conversational tone to the more complex like generating full texts from only a few prompts.
Simplified, a large language model receives text input, processes it according to the trained model, and then responds with text output.
In this post, we’ll discuss what LLMs are in more detail and how they’re being utilized to carry out a variety of tasks.
At their core, large language models are a type of artificial intelligence (AI) designed to mimic human intelligence. Large language models work by relying on statistical models to analyze immense amounts of data.
As they process this data, they learn the patterns and connections between characters, words, and phrases.
Then, they turn around and use what they’ve learned to generate original content. In essence, their goal is to predict what will come next.
A large language model is designed much like our own brains. It uses deep neural networks to generate outputs based on patterns learned during data training.
LLMs are an implementation of a transformer architecture, which allows a machine-learning model to identify relationships between words in a sentence. They’re able to do that through “self-attention mechanisms.”
Transformer neural networks use these mechanisms to capture the relationship between characters, words, sentences, and even segments in a sequence, regardless of their position in the text sequence.
The model then calculates the relationship between these tokens using attention scores. The higher the score, the more valuable a token is to surrounding tokens in a particular context.
The term “large” in large language models refers to the number of parameters, or values, that the model can take on autonomously with each task. This is how it can take huge amounts of data from one task and use it to develop its thinking process to carry out the following task.
The parameters are parts of the model learned from previous data. They’re basically the number of factors it has to consider when generating its output, which helps define its skill level on each particular task.
LLMs are trained with these immense bulks of data through a type of human feedback, known as a self-supervised learning process. It’s given a certain context and uses the data to predict the next character, word, or segment in a sentence, referred to as “tokens.” The more training data it’s given, the better and faster it’ll be at generating new content.
This process is repeated until the LLM reaches a decent degree of reliability.
The training process involves the following steps:
- Pre-process text, such as books, web pages, and articles.
- Convert it into numerical representations that can be fed into the model.
- Assign the model’s parameters randomly.
- Feed the numerical representation into the model.
- Use a loss function to calculate the difference between the model’s outputs and the next word in a sentence.
- Repeat this process to cut down data loss until the outputs reach an acceptable level of accuracy.
Once a large language model has been trained, it can be fine-tuned and adjusted to perform specific natural language processing tasks based on the parameters set by the user.
While this may seem like something straight out of a sci-fi novel, it’s not. Language modeling is becoming more popular due to the torrid pace of development over the past few years.
For example, if you want the model to generate an article in the style of Mark Twain, you provide the model with a prompt like a paragraph from one of Twain’s books.
Then, the LLM can generate the content you asked for by analyzing the connections and patterns it’s learned from that one paragraph.
Another example is using LLMs as virtual agents or chatbots. In this instance, they analyze natural language patterns from previous customer service responses. Then, they use the patterns to generate responses similar to how an actual human might reply according to the context.
The following are just a handful of examples of what LLMs can do:
- Build conversational chatbots, such as ChatGPT
- Answer frequently asked questions (FAQs) regarding various products and services
- Route customer queries to the proper customer-service personnel in the workplace
- Analyze customer feedback from product reviews, emails, and social media posts
- Classify and categorize large amounts of text data for more efficient analysis and process
- Translate business content into different languages
- Generate text for blog posts, articles, and product descriptions
- Recognize and synthesize speech patterns
- Text-speech synthesis
- Auto-correct spelling
- Summarize texts
- Image annotation
- Generate code
- Fraud detection
- Writing software code
- Improve productivity by automating your tasks through AI productivity software.
- Natural language processing applications – AI marketing software such as Neuronwriter.
Check out some of the more popular large language models currently available. There’s even a good chance you’ve used them once or twice without knowing.
- Chinchilla developed by DeepMind
- Generative Pretrained Transformer (GPT-3/GPT-4) developed by OpenAI
- Text-to-Text Transfer Transformer (T5) developed by Google
- Bidirectional Encoder Representations from Transformers (BERT) developed by Google
- Large Language Model Meta AI (LLaMA) developed by Facebook AI
- Megatron-Turing developed by NVIDIA
- Conditional Transformer Language Model (CTRL) developed by Salesforce Research
The possibilities of LLMs are endless. It seems that everyone wants a piece of this new technology, from the general public to investors and business owners.
Yet, like any technology, large language models aren’t without flaws. They currently also have their fair share of challenges and limitations.
One reason large language models are so remarkable is their ability to scale their performance the more parameters and data are added.
A second reason is that a pre-trained model is capable of making impressive predictions after receiving only a handful of prompts or examples.
Another benefit is that a single model can be used for several tasks. Some include summarizing documents, completing sentences, translating, answering questions, generating complete texts, and more!
For companies, this means a much more seamless process that saves both time and money because it reduces costs and manual labor. It also increases accuracy and improves efficiency in certain tasks, which translates into higher sales and customer satisfaction.
This leads us to the next benefit: enhanced personalization. Customers expect businesses to operate 24/7, and the only way they can do that is with the help of virtual assistants and chatbots that utilize language models. This increases availability and provides each customer with a personalized service.
One of the biggest challenges any large language model faces is making sure that the content it generates is authentic and well-founded. It can be super tricky to use when generating news articles and other types of content that require a high degree of authenticity.
It’s true that there are ways to mitigate this flaw, such as using AI plugins to link the model to a reliable source like a company website. By baking authenticity into the creation of the output, it becomes more efficient to harness an LLM’s generative abilities to create a range of useful content. This includes responses and training data that align with that particular company’s brand identity.
Yet, realistically speaking, these models are somewhat stochastic. In other words, they have a random distribution pattern that can be analyzed statistically speaking, but can’t be predicted 100% precisely.
As such, these models can not, at least for now, be 100% reliable or accurate.
Therefore, each piece of content generated by any large language model needs to be verified by a human, or a group of humans, before it’s sent out and communicated to the end user. This is especially crucial in enterprise settings where there are liability concerns.
Another drawback we should mention is that each large language model has a set amount of memory that it can accept as input. For example, ChatGPT as of the GPT-4 model only accepts 32000 tokens, which comes to about 25000 words. So, if you put in more than 25000 words, ChatGPT will lose context of prior words.
One problem with LLMs that many of us don’t consider is the impact they have on the environment. Almost every model was developed using hundreds of high-powered graphics processing unit (GPU) servers that require tens of kilowatts of power to operate. This requires a lot of power to cool down the hardware.
As a result, the training of these language models leaves behind huge carbon footprints that will negatively impact the environment for years to come.
Over millennia, humans have learned to develop language to communicate. It provides the tools needed to convey our thoughts and ideas.
In the AI world, LLMs serve a similar purpose. With their beginnings dating as far back as 1966 with MIT’s ELIZA model, the technology behind this generative AI is now more innovative and impactful than ever before.
One survey found that 60% of leaders in the tech industry said they increased their budgets for AI language technologies by more than 10%, whereas 33% of tech leaders reported an increase of nearly 30%.
These numbers are expected to rise exponentially with each passing year.
The most exciting part is that this technology is only getting started. There’s no end in sight to what these models can do.
To give you a glimpse of what LLMs can be programmed to do, a group of Google engineers recently published a research titled “Large Language Models Can Self-Improve.”
Through their research, they designed an LLM that can create a set of questions, and generate detailed answers to these questions. Then, the model filters its answers for the ones with the most high-quality input and fine-tunes itself according to it’s own curated answers.
Another example is based on an LLM method called “instruction fine-tuning.” This lies at the core of ChatGPT, which relies on human-written, natural language instructions.
Yet, what a group of researchers did is build an LLM that can generate its own instructions and then fine-tune itself according to those self-made instructions.
This idea that LLMs can create their own training data becomes particularly important in light of the fact that the world may run out of text training data.
There are about 4.6 trillion and 17.2 trillion tokens of usable text data in the world. This includes all the scientific papers, news articles, books, Wikipedia, publicly available codes, and many of the things published on the internet.
To put it into perspective, DeepMind’s chinchilla was trained using 1.4 trillion tokens. So, it’s highly likely that we’re close to exhausting the world’s entire supply of useful language training data.
If LLMs can generate their own training data, they’ll continue to improve and get smarter. As a result, they’ll become self-taught, which will take them on a mind-bending trajectory of progress.
If and when this becomes a reality, we may reach the point of AI singularity and Artificial General Intelligence (AGI).
This hasn’t happened yet, but it’s certainly a troubling thought that looms not only in the back of every AI researcher’s mind – but also in the minds of some of the most prolific tech entrepreneurs like Elon Musk.
As technology continues to advance, we’re constantly finding new ways to push the boundaries of what we once thought was impossible.
Large language models are only one instance of how we’re making use of new technologies to create AI software that’s more sophisticated, savvy, and self-regulating.