Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

Evaluating the factuality of long-form text generated by large language models (LMs) is non-
trivial because (1) generations often contain a mixture of supported and unsupported pieces …

The recent success of zero-and few-shot prompting with models like GPT-3 has led to a
paradigm shift in NLP research. In this paper, we study its impact on text summarization …

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

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

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 …

Despite significant progress in neural abstractive summarization, recent studies have shown
that the current models are prone to generating summaries that are unfaithful to the original …

Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-
trained language models and the availability of large-scale datasets. Despite promising …

Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge
bases and preventing hallucinations in sequence-to-sequence models. Drawing on humans' …

Recently proposed BERT-based evaluation metrics for text generation perform well on
standard benchmarks but are vulnerable to adversarial attacks, eg, relating to information …