We’re diving into the fascinating world of large language models (LLMs).
ChatGPT, Bard, Notion AI. If you’ve been keeping an eye on the field of natural language processing (NLP) lately, you’ve probably aware of the buzz surrounding these sleek AI models.
But what exactly are they, and how do they work?
You’re King Arthur, The LLM Is Merlin
Think of it like this, large language models are basically AI wizards capable of conjuring up human-like language.
Using these digital literary sorcerers, you have the ability to cast spells through the power of machine learning. But they’re not just pulling phrases out of thin air. They get training on massive amounts of text data, like books, articles, and websites.
During the training process, these models act like sponges. Which means they soak up all the patterns and structures of language they can find. Once they soak up enough, they can spit out new text that looks like a human wrote it up.
In fact, some portions of this post have been conjured by ChatGPT.
These models get training on so much data, they can do all sorts of language tasks, like translation, summarisation, and question-answering.
So, How Do They Do It?
You can look at the process in three stages:
- Fine-tuning, and
Pre-Training: During pre-training, the model learns from vast amounts of text data that stems from a variety of sources. This can include books, articles, and websites.
The goal of pretraining is to teach the model to recognise patterns and structures in language. Such as word meanings, grammar rules, and syntax. To make this work, a machine learning technique known as unsupervised learning is used. The model does not run on explicit instructions to learn, and rather uses statistical analysis to identify patterns and relationships in the data.
Pre-training can take weeks or even months, depending on the size and complexity of the model.
Fine-Tuning: Once the model has been pretrained, it can be fine-tuned for specific language tasks. Such as language translation, summarisation, or question-answering.
Fine tuning involves training the model on a smaller, more specific dataset that is tailor-fit to the task at hand.
For example, if the goal is to create a chatbot, the model can receive fine-tuning on conversational data to learn how to generate appropriate responses to user inputs. During fine tuning, the model takes on tweaking and optimisation to perform the specific task as accurately as possible.
Inference: Finally, during inference, the model generates new text based on what it has learned during pre-training and fine-tuning.
Inference involves inputting a prompt or query into the model and then generating a response or output based on the patterns and structures it has learned.
For example, if the model learns from news articles, it could generate a news article summary when given a longer article to process. The quality of the output during inference depends on the accuracy and specificity of the pretraining and fine tuning data.
One of the coolest things about large language models is that they can perform multiple language tasks with just one model. That means you can have a model that’s trained on translation data, but can also summarise and answer questions.
It’s versatility in its finest, sleekest form.
Concerns About the Ethical Implications of These Models.
The adage, “With great power comes great responsibility” couldn’t ring more true. Especially where the use of AI is concerned.
With the versatility that these LLMs offer, there’s a need for careful consideration surrounding complications in the ethical use of tools like ChatGPT.
Here are a number of ethical implications that these models come with:
- Misinformation: One ethical concern is that large language models may generate or propagate false or misleading information, which could have serious consequences for individuals or society as a whole.
- Bias: Another potential issue is that the models may perpetuate or even amplify existing biases or prejudices in language, which could harm marginalised groups or reinforce stereotypes.
- Privacy: There are concerns that large language models may pose privacy risks. Especially if they learn from sensitive or personal data, such as medical records or financial information.
- Regulation: Given the potential risks that come with large language models, there may be a need for regulatory oversight to ensure that there is responsibly and ethical use.
- Accountability: It may be difficult to hold large language models accountable for their actions or outputs, especially if they are highly complex and opaque. As such, there is a need for greater transparency and explainability in how these models are developed and used.
Researchers are, however, working on making these models more transparent and accountable, so we can reap the many benefits without any shady side effects.
Want To Learn More About LLMs?
Large language models are transforming the field of natural language processing and enabling a wide range of language tasks with human-like accuracy.
However, as with any powerful technology, there are also concerns about their ethical implications.
Thus, it’s absolutely crucial that we continue to explore and understand the potential risks and benefits of these models, as well as ways to make them more transparent and accountable.
To learn more about large language models and ChatGPT, we’ve got a variety of informative (and practical) articles, as well as videos available on our blog and on Udemy.
Brett St Clair’s Udemy courses on ChatGPT are a great resource for anyone that wants to learn more about these models and how they work.
So dive in, learn more, and make informed decisions about the future of NLP.