Sequence-to-sequence neural networks have recently achieved great success in abstractive
summarization, especially through fine-tuning large pre-trained language models on the …

While large-scale neural language models, such as GPT2 and BART, have achieved
impressive results on various text generation tasks, they tend to get stuck in undesirable …

There are a number of diverging hypotheses about the neural text degeneration problem, ie,
generating repetitive and dull loops, which makes this problem both interesting and …

This paper explores the elusive mechanism underpinning in-context learning in Large
Language Models (LLMs). Our work provides a novel perspective by examining in-context …

k-nearest-neighbor machine translation (NN-MT), proposed by Khandelwal et al.(2021), has
achieved many state-of-the-art results in machine translation tasks. Although effective, NN …

Unfaithful text generation is a common problem for text generation systems. In the case of
Data-to-Text (D2T) systems, the factuality of the generated text is particularly crucial for any …

Modern language models rely on the transformer architecture and attention mechanism to
perform language understanding and text generation. In this work, we study learning a 1 …

Large language models (LLMs), represented by ChatGPT, have greatly simplified text
generation tasks. However, they have also raised concerns about privacy risks such as data …

In this paper, we apply transformer-based Natural Language Generation (NLG) techniques
to the problem of text simplification. Currently, there are only a few German datasets …

Existing techniques often attempt to make knowledge transfer from a powerful machine
translation (MT) to speech translation (ST) model with some elaborate techniques, which …