Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encod

A Primer on Decoder-Only vs Encoder-Decoder Models for AI Translation

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2024-10-15 16:00:08

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks.

Encoder-decoder models, such as Google’s T5 and Meta’s BART, consist of two distinct components: an encoder and a decoder. The encoder processes the input (e.g., a sentence or document) and transforms it into numeral values that represent the meaning and the relationships between words.

This transformation is important because it allows the model to “understand” the input. Then, the decoder uses the information of the encoder and generates an output, such as a translation of the input sentence in another language or a summary of a document.

As Sebastian Raschka, ML and AI researcher, explained, encoder-decoder models “are particularly good at tasks where there is a complex mapping between the input and output sequences and where it is crucial to capture the relationships between the elements in both sequences” — such as translating from one language to another or summarizing long texts.

In contrast, decoder-only models, like OpenAI’s GPT family models, Google’s PaLM, or Meta’s Llama, consist solely of a decoder component. These models generate an output based on the input by predicting the next word or character in a sequence based on the previous words or characters, without the need for a separate encoding step.