Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Natural Language Generation (NLG) has made great progress in recent years due to the
development of deep learning techniques such as pre-trained language models. This …

Despite the success achieved in neural abstractive summarization based on pre-trained
language models, one unresolved issue is that the generated summaries are not always …

The state-of-the-art language model-based automatic metrics, eg BARTScore, benefiting
from large-scale contextualized pre-training, have been successfully used in a wide range of …

The Neural Machine Translation (NMT) model is essentially a joint language model
conditioned on both the source sentence and partial translation. Therefore, the NMT model …

Attention mechanisms have achieved substantial improvements in neural machine
translation by dynamically selecting relevant inputs for different predictions. However, recent …

Simultaneous machine translation (SiMT) starts its translation before reading the whole
source sentence and employs either fixed or adaptive policy to generate the target sentence …

Abstract In recent years, Recurrent Neural Network based Neural Machine Translation (RNN-
based NMT) equipped with an attention mechanism from the decoder to encoder, has …

Editing model parameters directly in Transformers makes updating open-source transformer-
based models possible without re-training (Meng et al., 2023). However, these editing …

Large language models (LLMs) exhibit hallucinations in long-form question-answering tasks
across various domains and wide applications. Current hallucination detection and …