Long-Context Large Language Models
Language modeling has captured the attention of researchers over the years, leading to numerous iterations and modifications of proposed architectures to address tasks in areas like machine translation, summarization, natural language understanding, sentiment analysis, and text labeling. Architectures have evolved from simple statistical models to recurrent networks and, more recently, transformers.
Despite variations in architecture styles, common research interests persist, including achieving better performance, reducing memory footprint, minimizing parameters for learning, ensuring stable learning, handling long-range textual inputs, and minimizing latency during inference. The transformer architecture, especially prevalent in language modeling, has been extensively explored to address these concerns.
In recent years, transformers, exemplified by models like GPT, BERT, ChatGPT, LLaMa, Pal, Bloom, Phi, Claude, and others, have gained prominence. These models typically incorporate attention mechanisms (both self and cross), residual connections, normalization techniques, feed-forward networks, and non-linearities as fundamental building blocks. Despite delivering unprecedented results compared to earlier modeling techniques, there remain challenges in the current implementations of transformers. This inlude the following:-
