What Are Large Language Models (LLMs)?
Large language models (LLMs) are powerful tools in natural language processing (NLP), helping us derive meaningful interactions with text and opening up various applications. In fact, our latest groundbreaking product, Wonder the VoC chatbot, uses generative AI based on LLMs. As we further advance these models, they will continue to play a pivotal role in our interaction with digital technology. In this article, let's try to understand large language models.
Language models have fundamentally transformed the landscape of natural language processing (NLP). These models are algorithms for generating sequences of words based on statistical probabilities learned from a massive amount of text data. When a language model is referred to as "large," it's usually because it has been trained on an incredibly vast amount of text (billions or even trillions of words). A large language model, therefore, is not just about the sheer amount of data it's trained on. It also concerns the size of the model's architecture itself – the number of parameters or 'learnable' weights that the model uses to make predictions. Models such as GPT-3 by OpenAI encompass billions of these parameters.
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Large language models are predominantly based on an architecture known as the Transformer. Transformer models read input text, paying varying amounts of 'attention' to different words in the sequence, and apply this attention to understand the context and meaning of words. In a training process akin to learning by example, these models learn patterns across the language and how words behave relative to each other in the sequence. For instance, given the input 'The weather is nice, let's go for a...', the language model, based on its training, might predict the next word as 'walk'.
From drafting emails, writing code, and answering queries to creating written content, large language models have myriad use cases. They underpin many of the NLP tools we use daily, including translation services, chatbots, and voice assistants. Despite their impressive capabilities, large language models do have certain limitations. For instance, they lack world knowledge beyond their training data and can sometimes produce incorrect or biased responses.
The field of large language models is continuously evolving. Ongoing research aims to make these models more accurate, unbiased, and controlled. Some research directions also focus on creating models that can explain their reasoning or learn more effectively from fewer examples. To see how LLMs work, talk to our VoC experts today.
