arXiv:2407.08770v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated great potential as generalist assistants, showcasing powerful task understanding and problem-solving capabilities. To deploy LLMs as AI assistants, it is crucial that these models exhibit desirable behavioral traits, such as non-toxicity and resilience against jailbreak attempts. Current methods for detoxification or preventing jailbreaking usually involve Supervised Fine-Tuning (SFT) or Reinforcement Learning from Human Feedback (RLHF), which requires finetuning billions of parameters through gradient descent with substantial computation cost. Furthermore, models modified through SFT and RLHF may deviate from the pretrained models, potentially leading to a degradation in foundational LLM capabilities. In this paper, we observe that surprisingly, directly editing a small subset of parameters can effectively modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreaking. Specifically, for a behavior that we aim to avoid, we employ a linear classifier, which we term the behavior probe, to classify binary behavior labels within the hidden state space of the LLM. Using this probe, we introduce an algorithm to identify a critical subset of LLM parameters that significantly influence this targeted behavior. Then we directly edit these selected parameters by shifting them towards the behavior probe. Such a direct parameter editing method necessitates only inference-level computational resources. Experiments demonstrate that in the representative detoxification task, our approach achieves reductions of up to 90.0% in toxicity on the RealToxicityPrompts dataset and 49.2% on ToxiGen, while maintaining the LLM’s general capabilities in areas such as common sense, question answering, and mathematics. Our code is available at https://github.com/lucywang720/model-surgery.
Analysis of Large Language Models and their Behavioral Traits
Large Language Models (LLMs) have shown remarkable potential as AI assistants, with their ability to understand tasks and solve problems. However, it is important for these models to exhibit desirable behavioral traits, such as non-toxicity and resistance to jailbreak attempts. Current methods for achieving this, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), require significant computational resources and may result in degradation of the LLM’s foundational capabilities.
In this paper, the authors propose a novel approach to modulating specific behaviors of LLMs, such as detoxification and resistance to jailbreaking. Instead of finetuning billions of parameters, the authors suggest directly editing a small subset of parameters, which significantly influences the targeted behavior. This direct parameter editing method requires only inference-level computational resources, making it more efficient.
To identify the subset of parameters, the authors introduce a behavior probe, which is a linear classifier that classifies binary behavior labels within the hidden state space of the LLM. By analyzing the behavior probe, the authors are able to identify the critical parameters that influence the targeted behavior. These parameters are then directly edited by shifting them towards the behavior probe.
The experiments conducted by the authors demonstrate the effectiveness of their approach. In a representative detoxification task, their method achieves substantial reductions in toxicity while maintaining the LLM’s general capabilities in areas such as common sense, question answering, and mathematics.
Multi-disciplinary Nature of the Concepts
This research paper combines concepts from various disciplines, highlighting the multi-disciplinary nature of the study. The authors draw from natural language processing, machine learning, and computer security to address the challenges of behavior modulation in LLMs.
Natural language processing techniques are utilized to understand and classify behaviors within the hidden state space of LLMs. Machine learning techniques, such as linear classifiers, are used to analyze and modify the LLM parameters. Finally, computer security principles are considered to ensure resistance against jailbreak attempts.
By leveraging insights from these different disciplines, the authors are able to propose a novel and efficient method for modifying LLM behaviors without compromising their foundational capabilities. This interdisciplinary approach is crucial in solving complex problems in the field of AI.