Large language models (LLMs) that have been trained on multilingual but not parallel text
exhibit a remarkable ability to translate between languages. We probe this ability in an in …

Large language models (LLMs) offer impressive performance in various zero-shot and few-
shot tasks. However, their success in zero-shot or few-shot settings may be affected by task …

Large language models (LLMs) are competitive with the state of the art on a wide range of
sentence-level translation datasets. However, their ability to translate paragraphs and …

Recent breakthroughs in large language models (LLMs) have centered around a handful of
data-rich languages. What does it take to broaden access to breakthroughs beyond first …

Foundational large language models (LLMs) can be instruction-tuned to perform open-
domain question answering, facilitating applications like chat assistants. While such efforts …

Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

While large language models have made remarkable advancements in natural language
generation, their potential in machine translation, especially when fine-tuned, remains under …

Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning
(ICL) are three alternative, de facto standard approaches to few-shot learning. ICL has …

This paper explores the role of Large Language Models (LLMs) in revolutionizing interactive
Machine Translation (MT), providing a comprehensive analysis across nine innovative …

Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose
language models capable of addressing many natural language generation or …