This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4)
research, state-of-the-art large language models (LLM) from the GPT series, and their …

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

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and
reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is …

Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for
natural language generation, but their performance for machine translation has not been …

ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies
have shown that it achieves comparable results to commercial systems for high-resource …

We describe GEMBA, a GPT-based metric for assessment of translation quality, which works
both with a reference translation and without. In our evaluation, we focus on zero-shot …

In this paper, we present the joint contribution of Unbabel and IST to the WMT 2022 Metrics
Shared Task. Our primary submission–dubbed COMET-22–is an ensemble between a …

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 demonstrated impressive capabilities in general
scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses …