Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

This survey reviews text-to-image diffusion models in the context that diffusion models have
emerged to be popular for a wide range of generative tasks. As a self-contained work, this …

In-context learning is a recent paradigm in natural language understanding, where a large
pre-trained language model (LM) observes a test instance and a few training examples as …

GPT-$3 $ has attracted lots of attention due to its superior performance across a wide range
of NLP tasks, especially with its powerful and versatile in-context few-shot learning ability …

Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue
models often suffer from factual incorrectness and hallucination of knowledge (Roller et al …

Large-scale generative models show an impressive ability to perform a wide range of
Natural Language Processing (NLP) tasks using in-context learning, where a few examples …

Existing work in translation demonstrated the potential of massively multilingual machine
translation by training a single model able to translate between any pair of languages …

Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3,
their performances still significantly underperform fine-tuned models in the task of text …

Novel architectures have recently improved generative image synthesis leading to excellent
visual quality in various tasks. Much of this success is due to the scalability of these …

This paper presents the results of the General Machine Translation Task organised as part
of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants …