Gated Recurrent Units (GRU) are a type of neural network that are commonly used in natural language processing (NLP) and speech recognition tasks. They are a variation of the more well-known Long Short-Term Memory (LSTM) networks, and were first introduced in a research paper by Cho et al. in 2014.

GRUs are designed to overcome some of the issues with traditional recurrent neural networks (RNNs) that can struggle to remember long-term dependencies between inputs. GRUs achieve this by incorporating gating mechanisms that selectively update and reset their hidden state in each time step. This allows them to capture important contextual information while also avoiding the vanishing gradient problem that can occur in deep networks.

One key advantage of GRUs over LSTMs is their simpler architecture, which makes them faster and more memory-efficient to train. Despite this, they have been shown to achieve similar performance to LSTMs on many NLP tasks, such as language modeling, machine translation, and sentiment analysis.

In summary, GRUs are a powerful tool for modeling sequential data, especially in the context of natural language processing. They offer a simpler alternative to LSTMs that can still achieve state-of-the-art results on many tasks.