Towards General Conceptual Model Editing via Adversarial Representation Engineering
<|reference_start|>Towards General Conceptual Model Editing via Adversarial Representation Engineering: Since the development of Large Language Models (LLMs) has achieved remarkable success, understanding and controlling their internal complex mechanisms has become an urgent problem. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to use representation engineering methods to guide the editing of LLMs by deploying a representation sensor as an oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an Adversarial Representation Engineering (ARE) framework to provide a unified and interpretable approach for conceptual model editing without compromising baseline performance. Experiments on multiple model editing paradigms demonstrate the effectiveness of ARE in various settings. Code and data are available at https://github.com/Zhang-Yihao/Adversarial-Representation-Engineering.<|reference_end|>
@article{zhang2024adversarial,
    title={Adversarial Representation Engineering: A General Model Editing
  Framework for Large Language Models},
    author={Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun},
    journal={arXiv preprint arXiv:2404.13752},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13752},
    primaryClass={cs.LG cs.AI cs.CL cs.CR math.OC}
}