Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models.However, their performance heavily relies on the manual design of prompts and thus requires a lot of human efforts.In this paper, we introduce Genetic Prompt Search (GPS) to improve few-shot learning with prompts, which utilizes a genetic algorithm to automatically search for high-performing prompts.GPS is gradient-free and requires no update of model parameters but only a small validation set.Experiments on diverse datasets proved the effectiveness of GPS, which outperforms manual prompts by a large margin of 2.6 points.Our method is also better than other parameter-efficient tuning methods such as prompt tuning.

BFAST (Breaks For Additive Seasonal and Trend) method and MODIS NDVI data were used to detect vegetation dynamics in Quebec during the period of 2000-2012. The Permanent Sample Plots (PSP) data were used to assess the detection method. The results demonstrated that 25.68% of the study area experienced NDVI trend changes during the research period. The detected timing of the biggest changes showed obviously that the areas with the biggest change in 2009 and 2002 were the top two with area percentages of 29.12% and 17.41%, respectively. The results suggested that abrupt vegetation greening occurred especially in 2009 with 58.33% of the overall abrupt greening. The abrupt vegetation browning occurred especially in 2002 with 28.22% of the overall abrupt browning. “Total cut” and “total burning” could be monitored easily using BFAST approach while “insect outbreak” and “plantation” could not be detected satisfyingly.