PID Control-Based Self-Healing to Improve the Robustness of Large
  Language Models
Zhuotong Chen Zihu Wang Yifan Yang Qianxiao Li Zheng Zhang
Despite the effectiveness of deep neural networks in numerous natural
language processing applications, recent findings have exposed the
vulnerability of these language models when minor perturbations are introduced.
While appearing semantically indistinguishable to humans, these perturbations
can significantly reduce the performance of well-trained language models,
raising concerns about the reliability of deploying them in safe-critical
situations. In this work, we construct a computationally efficient self-healing
process to correct undesired model behavior during online inference when
perturbations are applied to input data. This is formulated as a trajectory
optimization problem in which the internal states of the neural network layers
are automatically corrected using a PID (Proportional-Integral-Derivative)
control mechanism. The P controller targets immediate state adjustments, while
the I and D controllers consider past states and future dynamical trends,
respectively. We leverage the geometrical properties of the training data to
design effective linear PID controllers. This approach reduces the
computational cost to that of using just the P controller, instead of the full
PID control. Further, we introduce an analytical method for approximating the
optimal control solutions, enhancing the real-time inference capabilities of
this controlled system. Moreover, we conduct a theoretical error analysis of
the analytic solution in a simplified setting. The proposed PID control-based
self-healing is a low cost framework that improves the robustness of
pre-trained large language models, whether standard or robustly trained,
against a wide range of perturbations. A detailed implementation can be found
in:https://github.com/zhuotongchen/PID-Control-Based-Self-Healing-to-Improve-the-Robustness-of-Large-Language-Models.