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, ...

This repository contains the implementation of a machine translation model using Encoder-Decoder architecture with Long Short-Term Memory (LSTM) cells. The model is designed to translate text from one ...

We note that our work focuses on architectural comparisons rather than competing with recent SLM developments (e.g., SmolLM, MobileLLM). Our analysis isolates the fundamental advantages of ...

This comprehensive guide delves into decoder-based Large Language Models (LLMs), exploring their architecture, innovations, and applications in natural language processing. Highlighting the evolution ...

Our method leverages Connectionist Temporal Classification and a simple audio encoder to map the compressed acoustic features to the continuous semantic space of the LLM. In addition, we further probe ...

In the world of natural language processing, foundation models have typically come in 3 different flavors: Encoder-only (e.g. BERT), Encoder-Decoder (e.g. T5) and Decoder-only (e.g. GPT-*, LLaMA, PaLM ...

Abstract: Summary models, whether extractive or abstractive, have achieved great success recently. For long academic papers, the abstractive model with the encoder-decoder architecture mainly only ...

Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. However, the seamless ...