Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant,
and fluent answers for various natural language processing (NLP) tasks. Taking document …

Image inpainting has made significant advances in recent years. However, it is still
challenging to recover corrupted images with both vivid textures and reasonable structures …

Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding
light-weight adapter modules to a pretrained language model (PrLM) and only updating the …

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 …

Conditional image synthesis aims to create an image according to some multi-modal
guidance in the forms of textual descriptions, reference images, and image blocks to …

Non-autoregressive neural machine translation (NAMT) has received increasing attention
recently in virtue of its promising acceleration paradigm for fast decoding. However, these …

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 …

Though nearest neighbor Machine Translation ($ k $ NN-MT)\citep {khandelwal2020nearest
} has proved to introduce significant performance boosts over standard neural MT systems, it …

Prompting has recently been shown as a promising approach for applying pre-trained
language models to perform downstream tasks. We present Multi-Stage Prompting (MSP), a …

Rationale extraction can be considered as a straightforward method of improving the model
explainability, where rationales are a subsequence of the original inputs, and can be …