Abstract Foundation and Large Language Models (FLLMs) are models that are trained using
a massive amount of data with the intent to perform a variety of downstream tasks. FLLMs …

Abstract Machine translation is an important and challenging task that aims at automatically
translating natural language sentences from one language into another. Recently …

Autoregressive decoding of large language models (LLMs) is memory bandwidth bounded,
resulting in high latency and significant wastes of the parallel processing power of modern …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

With the emergence of foundation models (FMs) that are trained on large amounts of data at
scale and adaptable to a wide range of downstream applications, AI is experiencing a …

Text categorization and sentiment analysis are two of the most typical natural language
processing tasks with various emerging applications implemented and utilized in different …

Transformer-based autoregressive (AR) methods have achieved appealing performance for
varied sequence-to-sequence generation tasks, eg, neural machine translation …

Protein language models have demonstrated significant potential in the field of protein
engineering. However, current protein language models primarily operate at the residue …

Document-level neural machine translation (DocNMT) aims to generate translations that are
both coherent and cohesive, in contrast to its sentence-level counterpart. However, due to its …

Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation
(NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous …