The rapid development of Large Language Models (LLMs) like GPT-4, Claude-3, and
Gemini has transformed the field of natural language processing. However, it has also …

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 …

Research on prompting has shown excellent performance with little or even no supervised
training across many tasks. However, prompting for machine translation is still under …

Despite the fact that large-scale Language Models (LLM) have achieved SOTA
performances on a variety of NLP tasks, its performance on NER is still significantly below …

We introduce MADLAD-400, a manually audited, general domain 3T token monolingual
dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations …

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 …

Hallucinated translations can severely undermine and raise safety issues when machine
translation systems are deployed in the wild. Previous research on the topic focused on …

Consistency is a key requirement of high-quality translation. It is especially important to
adhere to pre-approved terminology and adapt to corrected translations in domain-specific …

Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant,
and fluent answers for various natural language processing (NLP) tasks. Taking document …

Large language models (LLMs) have demonstrated impressive capabilities in general
scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses …