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cs.CL2023★ 1 cited
Dissecting In-Context Learning of Translations in GPTs
Vikas Raunak, Hany Hassan Awadalla, Arul Menezes
Most of the recent work in leveraging Large Language Models (LLMs) such as GPT-3 for Machine Translation (MT) has focused on selecting the few-shot samples for prompting. In this w…
cs.CL2023★ 1 cited
Do GPTs Produce Less Literal Translations?
Vikas Raunak, Arul Menezes, Matt Post +1
Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the tas…
cs.CL2023★ 5 cited
Deliberate then Generate: Enhanced Prompting Framework for Text Generation
Bei Li, Rui Wang, Junliang Guo +7
Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts. While existing…