Reza Ghanadan

Oct 21, 2024 | amazon.science | Prasoon Goyal |Michael Johnston |Reza Ghanadan |Javier Chiyah-Garcia

In-Context Learning (ICL) has enabled Large Language Models (LLMs) to excel as generalpurpose models in zero and few-shot task settings. However, since LLMs are often not trained on the downstream tasks, they lack crucial contextual knowledge from the data distributions, which limits their task adaptability. This paper explores using data priors to automatically customize prompts in ICL.