Edit-based approaches for Grammatical Error Correction (GEC) have attracted volume attention due to their outstanding explanations of the correction process and rapid inference. Through exploring the characteristics of the generalized and specific knowledge learning for GEC, we discover that efficiently training GEC systems with satisfactory generalization capacity prefers more generalized knowledge rather than specific knowledge. Current gradient-based methods for training GEC systems, however, usually prioritize minimizing training loss over generalization loss. This paper proposes the strategy of Adjusting Learning Rate Based on Mermory Rate to optimize the edit-based GEC scorer (ALRMR-GEC). Specifically, we introduce the memory rate, a novel metric, to provide an explicit indicator for the model’s state of learning generalized and specific knowledge, which can effectively guide the GEC system to adjust the learning rate timely. Extensive experiments, conducted by optimizing the published edit scorer on the BEA2019 dataset, have shown our ALRMR-GEC significantly enhances the model generalization ability with stable and satisfactory performance nearly irrespective of the initial learning rate selection. Also, our method can accelerate the training over tenfold faster in certain cases. Finally, the experiments indicate the memory rate introduced in our ALRMR-GEC guides the GEC editscorer to learn more generalized knowledge.

Poison-only Clean-label Backdoor Attacks (PCBAs) aim to covertly inject attacker-desired behavior into DNNs by merely poisoning the dataset without changing the labels. To effectively implant a backdoor, multiple triggers are proposed for various attack requirements of Attack Success Rate (ASR) and stealthiness. Additionally, sample selection enhances clean-label backdoor attacks' ASR by meticulously selecting "hard'' samples instead of random samples to poison. Current methods, however, 1) usually handle the sample selection and triggers in isolation, leading to severely limited improvements on both ASR and stealthiness. Consequently, attacks exhibit unsatisfactory performance on evaluation metrics when converted to PCBAs via a mere stacking of methods. Therefore, we seek to explore the bi-directional collaborative relations between the sample selection and triggers to address the above dilemma. 2) Since the strong specificity within triggers, the simple combination of sample selection and triggers fails to substantially enhance both evaluation metrics, with generalization preserved among various attacks. Therefore, we seek to propose a set of components to significantly improve both stealthiness and ASR based on the commonalities of attacks. Specifically, Component A ascertains two critical selection factors, and then makes them an appropriate combination based on the trigger scale to select more reasonable "hard'' samples for improving ASR. Component B is proposed to select samples with similarities to relevant trigger implanted samples to promote stealthiness. Component C reassigns trigger poisoning intensity on RGB colors through distinct sensitivity of the human visual system to RGB for higher ASR, with stealthiness ensured by sample selection including Component B. Furthermore, all components can be strategically integrated into diverse PCBAs, enabling tailored solutions that balance ASR and stealthiness enhancement for specific attack requirements. Extensive experiments demonstrate the superiority of our components in stealthiness, ASR, and generalization. Our code will be released as soon as possible.