Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

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 …

Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

General-purpose language models have demonstrated impressive capabilities, performing
on par with state-of-the-art approaches on a range of downstream natural language …

The goal of information-seeking dialogue is to respond to seeker queries with natural
language utterances that are grounded on knowledge sources. However, dialogue systems …

Pre-trained language models (LMs) store knowledge in their parameters and can generate
informative responses when used in conversational systems. However, LMs suffer from the …

In this work, we propose a simple method that applies a large language model (LLM) to
large-scale retrieval in zero-shot scenarios. Our method, the Language language model as …

Relation extraction (RE) models have been challenged by their reliance on training data
with expensive annotations. Considering that summarization tasks aim at acquiring concise …

Knowledge-grounded dialogue systems are challenging to build due to the lack of training
data and heterogeneous knowledge sources. Existing systems perform poorly on unseen …