Language models like BERT excel at sentence classification tasks due to
extensive pre-training on general data, but their robustness to parameter
corruption is unexplored. To understand this better, we look at what happens if
a language model is "broken", in the sense that some of its parameters are
corrupted and then recovered by fine-tuning. Strategically corrupting BERT
variants at different levels, we find corrupted models struggle to fully
recover their original performance, with higher corruption causing more severe
degradation. Notably, bottom-layer corruption affecting fundamental linguistic
features is more detrimental than top-layer corruption. Our insights contribute
to understanding language model robustness and adaptability under adverse
conditions, informing strategies for developing resilient NLP systems against
parameter perturbations.

研究了语言模型（如 BERT）对参数故障的鲁棒性，发现底层的参数故障对语言模型的性能恢复有着更严重的影响，这些发现有助于了解语言模型在不利条件下的鲁棒性和适应能力，并为开发抵抗参数扰动的弹性自然语言处理系统提供了策略。

BERT 的治愈力量：如何通过任务特定微调恢复受损的语言模型

Healing Powers of BERT: How Task-Specific Fine-Tuning Recovers Corrupted  Language Models

Reliability of AI systems is a fundamental concern for the successful
deployment and widespread adoption of AI technologies. Unfortunately, the
escalating complexity and heterogeneity of AI hardware systems make them
inevitably and increasingly susceptible to hardware faults (e.g., bit flips)
that can potentially corrupt model parameters. Given this challenge, this paper
aims to answer a critical question: How likely is a parameter corruption to
result in an incorrect model output? To systematically answer this question, we
propose a novel quantitative metric, Parameter Vulnerability Factor (PVF),
inspired by architectural vulnerability factor (AVF) in computer architecture
community, aiming to standardize the quantification of AI model
resilience/vulnerability against parameter corruptions. We define a model
parameter's PVF as the probability that a corruption in that particular model
parameter will result in an incorrect output. Similar to AVF, this statistical
concept can be derived from statistically extensive and meaningful fault
injection (FI) experiments. In this paper, we present several use cases on
applying PVF to three types of tasks/models during inference -- recommendation
(DLRM), vision classification (CNN), and text classification (BERT). PVF can
provide pivotal insights to AI hardware designers in balancing the tradeoff
between fault protection and performance/efficiency such as mapping vulnerable
AI parameter components to well-protected hardware modules. PVF metric is
applicable to any AI model and has a potential to help unify and standardize AI
vulnerability/resilience evaluation practice.

AI 硬件系统的复杂性和异质性使其对硬件故障（如位翻转）变得越来越容易受损，因此本文提出了一种新的量化指标 PVF 以评估 AI 模型对参数损坏的脆弱性，该指标可帮助硬件设计人员在保护系统免受故障的同时平衡性能和效率。

PVF（参数脆弱性系数）：衡量 AI 对参数损坏的脆弱性和恢复性的量化指标

PVF (Parameter Vulnerability Factor): A Quantitative Metric Measuring AI  Vulnerability and Resilience Against Parameter Corruptions

We argue that the vulnerability of model parameters is of crucial value to
the study of model robustness and generalization but little research has been
devoted to understanding this matter. In this work, we propose an indicator to
measure the robustness of neural network parameters by exploiting their
vulnerability via parameter corruption. The proposed indicator describes the
maximum loss variation in the non-trivial worst-case scenario under parameter
corruption. For practical purposes, we give a gradient-based estimation, which
is far more effective than random corruption trials that can hardly induce the
worst accuracy degradation. Equipped with theoretical support and empirical
validation, we are able to systematically investigate the robustness of
different model parameters and reveal vulnerability of deep neural networks
that has been rarely paid attention to before. Moreover, we can enhance the
models accordingly with the proposed adversarial corruption-resistant training,
which not only improves the parameter robustness but also translates into
accuracy elevation.

本文探讨了模型参数的脆弱性对于模型鲁棒性和泛化性的重要性，并提出了通过参数损坏来衡量神经网络参数鲁棒性的指标，并给出了一种梯度下降的估算方法，以提高训练效率。我们的研究得到理论和实证的支持，发现了深度神经网络的脆弱性，并提出了一种敌对损坏抗性训练的方法，可以提高模型的鲁棒性和预测准确率。