A large language model (LLM) refers to a type of artificial intelligence (AI) model that has been trained on vast amounts of text data to respond in human-like language to text prompts.
LLMs are neural networks with billions of parameters that help it learn and understand words and sentences. They are designed to understand and then generate human language (and images, code, music...), allowing them to engage in conversations and help users in various tasks. They don't need to be explicitly told the ‘right’ answers, rather they figure it out on their own based on their training. (What is the Difference Between Generative AI and Conversational AI?
Trained on Huge Amounts of Data
During the training process, a large language model is exposed to enormous amounts of text from diverse sources, such as books, websites, articles, and more. By learning from this extensive dataset, the model becomes capable of predicting and generating coherent text based on prompts.
When we say "large" language model, we mean that it has a lot of connections between its 'neurons' (in deep learning, these are artificial neurons which are connection points in the neural network made up of mathematical functions). Think of it like having billions of little switches that help the model understand and generate language. Because of all these connections, LLMs are good at understanding and then responding to natural language in many different situations.
Even though LLMs are trained on clearly defined tasks like predicting the next word in a sentence, they end up learning a lot about how language works. They understand things like grammar and meaning. They also know a lot about the world because they learn from an extremely wide knowledge base. It's like they have a super memory!
Language models like GPT (Generative Pre-trained Transformer) are typically built using deep learning techniques, specifically using a type of neural network architecture known as a Transformer. These models consist of numerous layers of computation, allowing them to process and understand complex patterns and dependencies within language.
General Purpose Models
LLMs became popular around 2018 because they can do many different tasks really well. Before, people used to create separate programs for specific tasks like understanding emotions in text or picking out names of people or places. But LLMs are more general purpose models that allow us to use the one model to cover a variety of tasks.
Where are Large Language Models Used?
Large language models have numerous applications, including chatbots, virtual assistants, in content generation, language translation, question answering, and more. They provide a versatile and flexible tool for two-way communication between bots and humans, enabling a range of possibilities in Natural Language Processing and AI-powered conversational systems.
Why do we Need Custom Language Models?
Generalist Large Language Models like ChatGPT are good for engaging in broad, non-specific conversations. However, they do have a significant drawback - they can fabricate 'facts' when they are unsure of the answer, a phenomenon known as hallucination, about 15%-20% of the time. This can be a serious problem in industries where precision and accuracy are important, such as in customer engagement.
This is where Custom Language Models come in. These models are specifically trained using industry-focused data, making them highly accurate and precise. For instance, a Custom Language Model designed for AI chatbots in the credit and collections industry would need training on millions of actual customer conversations to understand the unique language and nuances of credit and collections. (For more information, refer to the article "Give AI Chatbots the Edge with Custom Language Models
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