Able to write emails for you, translate texts into different languages, code, compose poems, and more. It's impossible to ignore ChatGPT, the generative AI from OpenAI. But are you aware of the technology it's based on?
The Generative Pre-trained Transformer is an AI model excelling in tasks involving natural language processing. Explore pre-trained generative transformers, understand how they function, their benefits, and their limitations.
What is a pre-trained generative transformer (GPT)?
The pre-trained generative transformer encompasses a range of recurrent neural network models employing transformer architecture. This technology marks a significant milestone in the realm of generative artificial intelligence. The widespread adoption of ChatGPT is a testament to this. Since its introduction, all leading tech corporations have been vying to develop the most effective language model, aiming for a human-like experience.
Why such enthusiasm? Because this machine learning model is adept at executing various tasks associated with natural language processing. Its ability to simulate conversations between humans is sometimes eerily accurate. From comprehending queries to generating assorted types of coherent and relevant texts, it enables the simulation of a conversation with a human (almost).
In doing so, users can automate a myriad of tasks: linguistic translation, document summarization, blog article creation, social media content ideas, writing code, and even crafting poetry. There’s no need to spend countless hours on research, planning, and drafting; pre-trained generative transformers can handle these tasks in seconds.
Good to know: The transformer neural network architecture isn’t entirely new. It emerged from various research efforts on natural language processing and deep learning, with the term first being introduced in the 2017 paper “Attention is All You Need”.
How do pre-trained models work?
To achieve editorial feats comparable to (or sometimes surpassing) human capabilities, the pre-trained generative transformer relies on “transformer” neural network architecture.
It employs auto-regressive attention (or self-attention mechanism). This AI model considers not just the last word to generate text but the overall context. It can assign varying degrees of significance to words, thereby better discerning the relationships among words and sentences.
Ultimately, it’s this interplay of words and sentences that enables the GPT to comprehend the user’s query and deliver a coherent response, both in content and form.
Initially, the GPT model was pre-trained with extensive textual data to grasp the structure, syntax, and nuances of language. Only after achieving a solid understanding of human language was the model further trained to carry out specific tasks.
Good to know: Although pre-trained generative transformers generate human-like results, they remain machines. They analyze user queries and then predict the most suitable response based on their contextual understanding.
What are GPTs used for?
With increasing sophistication, pre-trained generative transformers can execute a broad spectrum of tasks. Below are some of their most common applications:
- Text generation: capable of crafting blog articles, social media posts, video scripts, emails, programming code, and more, in a variety of styles. Simply specify the desired outcome.
- Automatic translation: trained on billions of pieces of textual data, they can translate text into any language.
- Creation of sophisticated chatbots: acting as virtual assistants, they can answer any question posed by users.
- Summary extraction: given lengthy texts, they can generate concise summaries of around a hundred words.
- Data analysis: they can analyze vast datasets and transcribe them into tables or spreadsheets. Some tools even offer graphical representations.
For users, the real advantage of pre-trained generative transformers is their speed of execution. They accomplish in seconds what would take a human hours, thus significantly boosting productivity.
What are the limitations of pre-trained generative transformers?
Despite their utility and impressive efficiency, pre-trained generative transformers have their shortcomings, notably due to the training datasets. These may contain biases – be they sexist, racist, homophobic, etc. If these biases are incorporated into the model, it will replicate them in its outputs.
Therefore, it’s essential to approach its responses with caution. Ideally, verify the sources of the information (if the model provides them).
To mitigate these biases, it’s imperative to continually refine the models by feeding them unbiased data. This is a critical task for data scientists. If you’re keen on training the next GPT to yield better results, consider getting trained in data science.
