RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning. (arXiv:2205.12548v1 [cs.CL])

Prompting has shown impressive success in enabling large pretrained language
models (LMs) to perform diverse NLP tasks, especially when only few downstream
data are available. Automatically finding the optimal prompt for each task,
however, is challenging. Most existing work resorts to tuning soft prompt
(e.g., embeddings) which falls short of interpretability, reusability across
LMs, and applicability when gradients are not accessible. Discrete prompt, on
the other hand, is difficult to optimize, and is often created by "enumeration
(e.g., paraphrasing)-then-selection" heuristics …

arxiv learning reinforcement reinforcement learning text