A Large Language Model (LLM) is a type of artificial intelligence model specifically designed to understand and generate human-like text. LLMs are trained on a massive quantity of text data and are capable of producing compelling, contextually accurate outputs over a range of complex tasks.
Imagine if a super-smart parrot read lots of books and could repeat back parts in different ways, or even make up new sentences that sound like they come from a book. That’s essentially how a Large Language Model works. It’s like a giant word puzzle solver.
A Large Language Model (LLM), a subtype of machine learning models, leverages an expansive dataset of text to understand, interpret and produce text that can convincingly replicate human language. This is achieved through intricate mathematical operations that allow the model to ’learn’ patterns in the data.
Unlike standard language models, LLMs are trained on a larger scale of data, often encompassing millions to billions of words. This massive datastore empowers them to generate high-quality text that is contextually aware and grammatically refined. An quintessential example of an LLM is OpenAI’s GPT-3 which counts 175 billion machine learning parameters.
LLMs employ a type of neural network known as a transformer, which enables attention mechanisms allowing the models to weigh the importance of words in a sentence during training. This helps the model to effectively capture the semantic relationships between elements of text and to build more robust representations. It revolves around the concept of tokens, where each token represents a word or character.
During training, models receive sequences of tokens and aim to predict each next token by conditioning on the preceding ones. Errors between the prediction and actual tokens are backpropagated throughout the network, facilitating the iterative adjustment of model parameters.
Post-training, an LLM can perform a multitude of tasks, such as translation, summarization, and question-answering. This is possible due to their inherent capability of zero-shot learning, where they can conduct efficient inferences without explicit re-training, utilizing their acquired raw text knowledge.
However, LLMs come with trade-offs. These include immense computational requirements, difficulties in comprehending model decisions, and the baked-in biases from the training data that may result in ethically contentious outputs.
Transformer, Attention Mechanism, Neural Networks, Tokens, Backpropagation, Zero-shot Learning, GPT-3, NLP, Machine Learning (ML),, Bias in AI.