Understanding large language models
THIS past week, I listened to Bill Gates respond to CNN international correspondent Larry Madowo’s question about whether he genuinely thought artificial intelligence (AI) was a game changer.
HIS response inspired me to write this article. “Well, it’s very early days in AI,”
Gates said.
“And that’s why it’s so impressive that already we see these 50 innovators here in Africa after the first LLM breakthrough, uh, ChatGPT.”
Gates used the abbreviation LLM as if it was a common word, but how many people know what it stands for or what it means?
So, this week, I will talk about large language models (LLMs). In simple terms, an LLM is a type of AI that can generate and understand text. It is like a super-smart computer that can read and write like a human.
LLMs are trained on massive amounts of text data. This data can include books, articles, websites and even code. The more data an LLM is trained on, the better it will be at understanding and generating text.
LLMs are deep learning algorithms that can recognise, summarise, translate, predict and generate content using very large datasets.
LLMs are also called neural networks (NNs), computing systems inspired by the human brain. These neural networks work using a network of layered nodes, much like neurons.
LLMs are based on transformer models and are trained on vast amounts of data, which makes them large. This allows them to understand, translate, predict or create text or other content.
Another term is transformer model. A transformer model is the most common structure of an LLM. It has an encoder and a decoder.
A transformer model processes data by breaking the input into tokens and then performing mathematical calculations to determine how the tokens are related. This lets the computer see the patterns a human would notice if they had the same query.
Before an LLM can process text input and generate output content, it must undergo training and fine-tuning.
Training
LLMs are pre-trained on vast amounts of text data from sources like Wikipedia and GitHub.
These data contain trillions of words, and their quality affects the model’s performance. At this stage, the LLM learns without specific instructions. It learns the meaning and the relationships of words and how to use them in different contexts. For example, it learns to understand whether “leave” means “a day off ” or “to depart”.
Fine-tuning
To make an LLM do a specific task, such as translation, it must be fine-tuned. Fine-tuning improves the model’s performance for specific tasks. Prompt-tuning is like fine-tuning, but it uses fewer or no examples to train the model for a specific task. It uses natural language prompts to guide the model’s output.
LLMs are the power behind generative AI, like ChatGPT, which can generate text based on inputs. They can produce an example of text when prompted. For example: “Tell me about the Zimbabwean Economy.”
Large language models empower customer service chatbots or conversational AI to interact with customers, understand the intent and meaning of their questions or feedback, and provide relevant and helpful answers in return.
Challenges of LLMs
LLMs are not perfect, and they face a variety of challenges, including:
1. Hallucinations: LLMs can sometimes generate false outputs that do not match the user’s intent. This is because LLMs are trained to predict the next word or phrase that is grammatically correct, but sometimes, they cannot fully understand human meaning.
2. Bias: LLMs are trained on massive datasets of text, which can reflect the biases of the real world. This means LLMs can sometimes generate outputs that are biased or offensive.
3. Safety: LLMs can generate harmful content, such as disinformation, hate speech and spam. Developing safeguards to prevent LLMs from being used for malicious purposes is essential.
Despite these challenges, LLMs are a powerful new technology with the potential to revolutionise the way we interact with computers.
Researchers are working on ways of mitigating the challenges of LLMs. As LLMs continue to improve, they are likely to become increasingly helpful and widespread.