Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

This article offers a comprehensive review of the research on Natural Language Generation
(NLG) over the past two decades, especially in relation to data-to-text generation and text-to …

The quality of texts generated by natural language generation (NLG) systems is hard to
measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the
hallucination of large language models (LLMs). However, existing research lacks rigorous …

In the summarization domain, a key requirement for summaries is to be factually consistent
with the input document. Previous work has found that natural language inference (NLI) …

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

Language models (LMs) now excel at many tasks such as few-shot learning, question
answering, reasoning, and dialog. However, they sometimes generate unsupported or …

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

The Generative Pre-trained Transformer (GPT) models, renowned for generating human-like
text, occasionally produce “hallucinations”-outputs that diverge from human expectations …