LLMs are natural language processing models that analyze vast amounts of text data and infer connections between words within the text. Over the past few years, advances in computational power have allowed these machine learning models to grow in size and capability. The more extensive the input dataset and parameter space, the more robust LLMs become.Language models are typically trained using next-token prediction. This technique involves predicting the next word in a sequence using the Long-Short-Term-Memory (LSTM) neural network and masked-language modeling. The latter involves completing a sentence by selecting the most statistically probable word based on the context.This short video explains the tech behind LLMs:However, these sequential techniques have some limitations.For instance, the model may fail to prioritize certain words over others, leading to inaccurate predictions. Besides, input data is processed sequentially rather than as a whole corpus. This limits its ability to understand word connections and deduce the possible meanings.These issues can be addressed through advanced techniques such as self-attention mechanisms, which enable large language models like GPT-3 to process input data as a whole and derive more nuanced relationships between words.To tackle this problem, in 2017, a team from Google Brain introduced transformers. The transformer deep learning model is trained to perform multiple tasks simultaneously, and attention mechanisms enable it to selectively allocate resources to each task based on their relative importance.Transformers can process all input data simultaneously, which is impossible with LSTMs. With the self-attention mechanism, the model can assign different levels of significance to various input data segments, regardless of the position in the text sequence. The ability to perform simultaneous processing allowed for significant improvements in embedding meaning into LLMs and made it possible to handle much larger datasets. Just as a greater vocabulary allows educated humans to express themselves more effectively, larger semantic datasets provide chatbots with more opportunities to put together appropriate word combinations.