Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Transformer architecture has shown impressive performance in multiple research domains
and has become the backbone of many neural network models. However, there is limited …

Rotary Position Embeddings (RoPE) have been shown to effectively encode positional
information in transformer-based language models. However, these models fail to …

In recent years, there have been remarkable advancements in the performance of
Transformer-based Large Language Models (LLMs) across various domains. As these LLMs …

Recurrent neural networks (RNNs) have fast inference and scale efficiently on long
sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with …

This work introduces an efficient method to scale Transformer-based Large Language
Models (LLMs) to infinitely long inputs with bounded memory and computation. A key …

Transformers are the dominant architecture for sequence modeling, but there is growing
interest in models that use a fixed-size latent state that does not depend on the sequence …