RNNs too are neural networks which handle sequential data by maintaining an internal state. Here the input of varying length is processed.

LLMs lean to predict the probability of a word given its context in a sentence or sequence of words. Such models are GPT-series using transformer architecture to learn contextual representations of words so as to generate text.

Both RNNs and LLMs handle sequential data, but LLMs do it better because they capture long-range dependencies and understand content more effectively. LLMs do more efficient work because of their attention mechanisms. These allow them to attend to all positions in the input sequence simultaneously. They understand context of each word in relation to all other words in a sequence, irrespective of the fact how apart they are in the sequence.

Long range dependencies refer to relationships between words (tokens) in a sequence which are separated by so many other words. Capturing such dependencies is crucial for understanding the context and meaning in language.

The prisoner they have kept isolated in the cell is my brother. Here the word ‘brother’ depends on ‘isolated’ and ‘cell’. All these words are separated by other words. RNNs struggle with long-range dependencies. The reason being RNNs process input sequentially and find it difficult to retain information over long distances in the sequence.

LLMs can handle large amounts of data, especially with transformer architecture, by doing parallel processing. The processing of all elements is simultaneous. They attend to different parts of the input sequence in parallel. They are thus faster and take less inference time. They are scalable and practical.

RNNs are the building blocks while LLMs are finished structures built using RNN blocks. RNNs are still in use in Machine Translation, Speech Recognition and Sentiment Analysis.

In past Google Translate used a type of RNN called LSTM in its machine translation. It was called Google Neural Machine Translation (GNMT). Of late, Google Translate moved on more advanced architectures such as transformers to achieve better translation quality.