GitHub

Return of the Encoder: Efficient Small Language Models

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 ...
Slator

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

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, ...
GitHub

understanding-of-LLM-Encoder-and-decoder-model-

An Encoder-Decoder model is a fundamental architecture in the field of deep learning and natural language processing (NLP). It's widely used for a variety of tasks, including machine translation, text ...
IEEE

Encoder–Decoder Calibration for Multimodal Machine Translation

Abstract: The main purpose of multimodal machine translation (MMT) is to improve the quality of translation results by taking the corresponding visual context as an additional input. Recently many ...
IEEE

Encoder-Decoder Based Route Generation Model for Flexible Travel Recommendation

Abstract: Travel route recommendation is an important part of electronic tour guides and map applications. It aims to recommend a sequence of points of interest (POIs) to users based on their ...
Microsoft

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder ...
Frontiers

Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design

Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins.