An overview of LLMs and their challenges
Large language models (LLMs) are a type of deep neural network models that can process and generate natural language at a large scale.
They have become increasingly popular and powerful in recent years, achieving spectacular results on various natural language processing (NLP) tasks, such as machine translation, text summarization, question answering, sentiment analysis, etc. Some examples of LLMs are BERT, GPT, T5 and XLNet.
LLMs are based on the transformer architecture, which is a novel way of modeling sequential data using self-attention mechanisms. Self-attention allows the model to learn the dependencies and relationships between different parts of the input and output sequences, without relying on recurrent or convolutional layers.
Transformer models can be divided into two types: encoder-only models and encoder-decoder models. Encoder-only models, such as BERT and XLNet, take an input sequence and produce a contextualized representation of it, which can be used for downstream tasks such as classification or extraction. Encoder-decoder models, such as GPT and T5, take an input sequence and generate an output sequence, which can be used for tasks such as generation or translation.
Trained using massive amounts of data
LLMs are trained using massive amounts of text data, mostly scraped from the Internet. The data sources include websites, books, news articles, social media posts, etc. The data collection and cleaning methods vary depending on the model and the task. For example, BERT uses Wikipedia and BookCorpus as its data sources, and applies some preprocessing steps such as tokenization, masking, and segmentation. GPT uses a larger and more diverse dataset called WebText, which is filtered from Common Crawl using a heuristic to remove low-quality content. T5 uses a dataset called C4, which is also derived from Common Crawl, but uses a more sophisticated filtering process based on natural language understanding.
The training of LLMs requires a lot of computational resources and time. For example, BERT…