Evaluating natural language generation (NLG) is a vital but challenging problem in artificial
intelligence. Traditional evaluation metrics mainly capturing content (eg n-gram) overlap …

Widely used learned metrics for machine translation evaluation, such as Comet and Bleurt,
estimate the quality of a translation hypothesis by providing a single sentence-level score …

Generative large language models (LLMs), eg, ChatGPT, have demonstrated remarkable
proficiency across several NLP tasks, such as machine translation, text summarization …

Large language models (LLMs) have significantly advanced various natural language
processing (NLP) tasks. Recent research indicates that moderately-sized LLMs often …

Large Language Models (LLMs) demonstrate remarkable translation capabilities in high-
resource language tasks, yet their performance in low-resource languages is hindered by …

Ten years ago a single metric, BLEU, governed progress in machine translation research.
For better or worse, there is no such consensus today, and consequently it is difficult for …

Large Language Models (LLMs) have achieved impressive results in Machine Translation
(MT). However, careful evaluations by human reveal that the translations produced by LLMs …

Despite the recent success of automatic metrics for assessing translation quality, their
application in evaluating the quality of machine-translated chats has been limited. Unlike …

Recent machine translation (MT) metrics calibrate their effectiveness by correlating with
human judgment. However, these results are often obtained by averaging predictions across …

Large language models (LLMs) have revolutionized the field of NLP. Notably, their in-
context learning capabilities also enable their use as evaluation metrics for natural language …