A Recurrent Neural Network (RNN) is a type of artificial neural network designed for processing sequential data. It excels in tasks like speech recognition and time-series analysis by processing data step-by-step, with each step’s output influencing the next. This structure allows RNNs to maintain a continuous flow of information, adapting to the sequence’s context and length.
RNNs and Large Language Models (LLMs) like GPT3, built on the Transformer architecture, are pivotal in generative AI technologies but differ significantly in handling data sequences. RNNs are designed for sequential data processing, effective in applications like speech recognition and time-series analysis. They process data step-by-step, with each step influencing the next, maintaining a continuous information flow. In contrast, LLMs manage extensive text data efficiently and generate text resembling human writing. Unlike RNNs, LLMs process data concurrently, allowing them to handle longer sequences more effectively, though this requires greater computational resources and complexity.
LLMs’ concurrent processing means they can handle multiple parts of a data sequence at once, unlike the sequential method of RNNs. For instance, while RNNs analyze text word by word, LLMs can process several words or sentences simultaneously. This ability makes LLMs more adept at grasping context and relationships in longer text sequences, crucial for tasks like text generation or translation.
Here are some examples of systems in current use which are based on RNN architecture: Google for voice search and translation, Apple’s Siri and Amazon’s Alexa for voice recognition, IBM Watson for language processing, Microsoft Cortana and SwiftKey for natural language understanding and Facebook AI for content analysis.
Reference:
https://arxiv.org/abs/1912.05911
See Also: Feed Forward Network