This survey article proposes a clearer view of Natural Language Reasoning (NLR) in the
field of Natural Language Processing (NLP), both conceptually and practically …

In this article, we introduce and discuss the pervasive issue of bias in the large language
models that are currently at the core of mainstream approaches to Natural Language …

Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural
Language Generation (NLG), ie, evaluating the generated text from multiple explainable …

There has been growing interest in parameter-efficient methods to apply pre-trained
language models to downstream tasks. Building on the Prompt Tuning approach of Lester et …

Grounded text generation systems often generate text that contains factual inconsistencies,
hindering their real-world applicability. Automatic factual consistency evaluation may help …

Despite the recent success of multi-task learning and transfer learning for natural language
processing (NLP), few works have systematically studied the effect of scaling up the number …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

This paper reexamines the research on out-of-distribution (OOD) robustness in the field of
NLP. We find that the distribution shift settings in previous studies commonly lack adequate …

Factual consistency is an essential quality of text summarization models in practical settings.
Existing work in evaluating this dimension can be broadly categorized into two lines of …

Many text generation applications require the generated text to be factually consistent with
input information. Automatic evaluation of factual consistency is challenging. Previous work …