PID Control-Based Self-Healing to Improve the Robustness of Large Language Models

• URL: http://arxiv.org/abs/2404.00828v1
• Date: Sun, 31 Mar 2024 23:46:51 GMT
• Title: PID Control-Based Self-Healing to Improve the Robustness of Large Language Models
• Authors: Zhuotong Chen, Zihu Wang, Yifan Yang, Qianxiao Li, Zheng Zhang,
• Abstract summary: Minor perturbations can significantly reduce the performance of well-trained language models. We construct a computationally efficient self-healing process to correct undesired model behavior. The proposed PID control-based self-healing is a low cost framework that improves the robustness of pre-trained large language models.
• Score: 23.418411870842178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments [50.310636905746975]
  Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process. Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their reason-agnostic nature. We propose self-healing machine learning (SHML) to overcome these limitations.
  arXiv  Detail & Related papers  (2024-10-31T20:05:51Z)
• Bisimulation metric for Model Predictive Control [44.301098448479195]
  Bisimulation Metric for Model Predictive Control (BS-MPC) is a novel approach that incorporates bisimulation metric loss in its objective function to directly optimize the encoder. BS-MPC improves training stability, robustness against input noise, and computational efficiency by reducing training time. We evaluate BS-MPC on both continuous control and image-based tasks from the DeepMind Control Suite.
  arXiv  Detail & Related papers  (2024-10-06T17:12:10Z)
• Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control [0.0]
  Proportional-Integrator-Derivative (PID) controller is used in a wide range of industrial and experimental processes. Due to the uncertainty of model parameters and external disturbances, real systems such as Quadrotors need more robust and reliable PID controllers. In this research, a self-tuning PID controller using a Reinforcement-Learning-based Neural Network has been investigated.
  arXiv  Detail & Related papers  (2023-07-03T19:35:52Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• Adaptive Model Predictive Control by Learning Classifiers [26.052368583196426]
  We propose an adaptive MPC variant that automatically estimates control and model parameters. We leverage recent results showing that BO can be formulated as a density ratio estimation. This is then integrated into a model predictive path integral control framework yielding robust controllers for a variety of challenging robotics tasks.
  arXiv  Detail & Related papers  (2022-03-13T23:22:12Z)
• Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators [0.0]
  We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. We present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. We present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system.
  arXiv  Detail & Related papers  (2021-09-22T15:31:24Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Control of Stochastic Quantum Dynamics with Differentiable Programming [0.0]
  We propose a framework for the automated design of control schemes based on differentiable programming. We apply this approach to state preparation and stabilization of a qubit subjected to homodyne detection. Despite the resulting poor signal-to-noise ratio, we can train our controller to prepare and stabilize the qubit to a target state with a mean fidelity around 85%.
  arXiv  Detail & Related papers  (2021-01-04T19:00:03Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)
• Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven [80.94390916562179]
  Time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. It is desirable to prevent the time-driven dHDP from updating due to insignificant system event such as noise. We show how the event-driven dHDP algorithm works in comparison to the original time-driven dHDP.
  arXiv  Detail & Related papers  (2020-06-16T05:51:25Z)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.