Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

The emergence of ChatGPT has generated much speculation in the press about its potential
to disrupt social and economic systems. Its astonishing language ability has aroused strong …

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 …

Recently, the emergence of ChatGPT has attracted wide attention from the computational
linguistics community. Many prior studies have shown that ChatGPT achieves remarkable …

A wide variety of NLP applications, such as machine translation, summarization, and dialog,
involve text generation. One major challenge for these applications is how to evaluate …

The rapid development of Large Language Models (LLMs) has substantially expanded the
range of tasks they can address. In the field of Natural Language Processing (NLP) …

Long documents such as academic articles and business reports have been the standard
format to detail out important issues and complicated subjects that require extra attention. An …

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

Evaluating text summarization is a challenging problem, and existing evaluation metrics are
far from satisfactory. In this study, we explored ChatGPT's ability to perform human-like …

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 …