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

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

Automatic evaluation of generated textual content presents an ongoing challenge within the
field of NLP. Given the impressive capabilities of modern language models (LMs) across …

Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-
many problem, ie, many appropriate responses other than just the golden response. As of …

Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging
task due to its subjectivity. Designing an evaluation metric that is as objective as possible is …

Reinforcement learning from human feedback (RLHF) is a recent technique to improve the
quality of the text generated by a language model, making it closer to what humans would …

Automatic text summarization is the process of shortening a large document into a summary
text that preserves the main concepts and key points of the original document. Due to the …

The zero-shot capability of Large Language Models (LLMs) has enabled highly flexible,
reference-free metrics for various tasks, making LLM evaluators common tools in NLP …

The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs)
has marked a new era of Natural Language Processing (NLP), introducing unprecedented …

Extensive efforts in the past have been directed toward the development of summarization
datasets. However, a predominant number of these resources have been (semi) …