Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

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

Large language models have led to remarkable progress on many NLP tasks, and
researchers are turning to ever-larger text corpora to train them. Some of the largest corpora …

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

It is well known that the standard likelihood training and approximate decoding objectives in
neural text generation models lead to less human-like responses for open-ended tasks such …

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

We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training
examples that proposes a controlled generation task: given a Wikipedia table and a set of …

Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples
into natural text, focused on domain-specific benchmark datasets. In this paper, however, we …

The widespread adoption of large language models (LLMs) makes it important to recognize
their strengths and limitations. We argue that in order to develop a holistic understanding of …

Abstract Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known
as multitask-prompted fine-tuning (MT), have shown capabilities to generalize to unseen …