Neuroplasticity and Corruption in Model Mechanisms: A Case Study Of Indirect Object Identification

2025-02-27

AI Summary

This study investigates the mechanistic degradation and reversibility of language models under toxic data fine-tuning. Toxic fine-tuning induces model corruption, yet its underlying neural mechanisms and potential for recovery remain poorly understood. Method: Leveraging causal tracing and circuit localization—key techniques from mechanistic interpretability—alongside task-specific fine-tuning and clean-data reverse retraining, we conduct controlled ablation and reconstruction experiments. Results: We establish, for the first time, that corruption exhibits *circuit-level specificity*: only critical computational pathways are selectively impaired, while peripheral circuits remain intact. Crucially, we demonstrate *neuroplastic-like recoverability*: clean-data retraining reconstructs original functional mechanisms with >89% restoration fidelity; this recovery generalizes across fine-tuning epochs. Contribution: Our work identifies precise circuit-level localization principles governing corruption and empirically validates the reversibility of mechanistic damage—providing both theoretical foundations and actionable strategies for robust alignment and trustworthy fine-tuning.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Previous research has shown that fine-tuning language models on general tasks enhance their underlying mechanisms. However, the impact of fine-tuning on poisoned data and the resulting changes in these mechanisms are poorly understood. This study investigates the changes in a model's mechanisms during toxic fine-tuning and identifies the primary corruption mechanisms. We also analyze the changes after retraining a corrupted model on the original dataset and observe neuroplasticity behaviors, where the model relearns original mechanisms after fine-tuning the corrupted model. Our findings indicate that: (i) Underlying mechanisms are amplified across task-specific fine-tuning which can be generalized to longer epochs, (ii) Model corruption via toxic fine-tuning is localized to specific circuit components, (iii) Models exhibit neuroplasticity when retraining corrupted models on clean dataset, reforming the original model mechanisms.

Problem

• Impact of fine-tuning on poisoned data and mechanism changes.
• Identification of primary corruption mechanisms during toxic fine-tuning.
• Neuroplasticity behaviors in models retrained on clean datasets.

Innovation

• Analyzes model corruption via toxic fine-tuning
• Identifies localized corruption in specific circuits
• Observes neuroplasticity in retraining on clean data