RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning

arXiv:2305.14502v2 Announce Type: replace-cross
Abstract: Recent developments in large pre-trained language models have enabled unprecedented performance on a variety of downstream tasks. Achieving best performance with these models often leverages in-context learning, where a model performs a (possibly new) task given one or more examples. However, recent work has shown that the choice of examples can have a large impact on task performance and that finding an optimal set of examples is non-trivial. While there are many existing methods for …

abstract arxiv context cs.ai cs.cl cs.lg examples however in-context learning language language models performance reinforcement reinforcement learning retrieval tasks type work