Detoxifying Large Language Models via Knowledge Editing (2403.14472v1)

Abstract: This paper investigates using knowledge editing techniques to detoxify LLMs. We construct a benchmark, SafeEdit, which covers nine unsafe categories with various powerful attack prompts and equips comprehensive metrics for systematic evaluation. We conduct experiments to compare knowledge editing approaches with previous baselines, indicating that knowledge editing has the potential to efficiently detoxify LLMs with limited impact on general performance. Then, we propose a simple yet effective baseline, dubbed Detoxifying with Intraoperative Neural Monitoring (DINM), to diminish the toxicity of LLMs within a few tuning steps via only one instance. We further provide an in-depth analysis of the internal mechanism for various detoxify approaches, demonstrating that previous methods like SFT and DPO may merely suppress the activations of toxic parameters, while DINM mitigates the toxicity of the toxic parameters to a certain extent, making permanent adjustments. We hope that these insights could shed light on future work of developing detoxifying approaches and the underlying knowledge mechanisms of LLMs. Code and benchmark are available at https://github.com/zjunlp/EasyEdit.

Detoxifying LLMs through Knowledge Editing

Introduction to the Research

The continuous advancement of LLMs has presented pressing concerns regarding their potential to generate harmful content. This paper explores the utilization of knowledge editing techniques as a novel approach to detoxify LLMs while preserving their general performance capabilities. By constructing a comprehensive benchmark named SafeEdit, the paper systematically evaluates various knowledge editing methodologies against conventional baselines, presenting an innovative method, Detoxifying with Intraoperative Neural Monitoring (DINM), that proves effective in mitigating the toxicity of LLMs with minimal impact on their overall functionality.

Constructing the SafeEdit Benchmark

SafeEdit encompasses a diverse array of unsafe categories coupled with potent attack prompts, serving as a novel benchmark for evaluating the efficiency of knowledge editing in detoxifying LLMs. This section explores the intricacies of benchmark construction, including task definitions, dataset creation, and the formulation of comprehensive evaluation metrics. Key aspects of SafeEdit include:

• A systematic arrangement covering nine unsafe categories equipped with powerful attack prompts.
• The introduction of evaluation metrics that extend beyond the conventional scope to include defense success, defense generalization, and the preservation of general performance post-detoxification.
• The demonstration of knowledge editing's potential through experiments on prominent LLMs, highlighting the method's efficiency and minimal impact on general performance.

Proposing the DINM Method

DINM stands out as a straightforward yet potent baseline that significantly reduces the toxicity in LLMs through precise toxic region editing. This method identifies toxic regions within LLMs based on contextual semantics, employing a single instance to mitigate toxicity effectively. The paper elaborates on DINM’s mechanism, emphasizing its ability to not only suppress but substantially reduce the toxicity inherent in the toxic parameters. The proposed approach underscores a promising direction for future research in developing detoxifying strategies rooted in the understanding of LLM’s knowledge mechanisms.

Experimental Findings and Implications

The empirical investigation underscores the efficacy of DINM in comparison to traditional detoxifying methods. Significant numerical results highlight the superiority of knowledge editing techniques in achieving improved detoxification success rates without compromising the LLMs’ general abilities. The research provides a deep dive into the detoxification mechanisms, revealing that while methods like SFT and DPO merely suppress toxic activations, DINM actively modifies toxic parameters resulting in a more definitive detoxification. These insights pave the way for a deeper understanding and further exploration into enhancing the safety and ethical use of LLMs.

Theoretical and Practical Contributions

This paper contributes to the ongoing discourse on the detoxification of LLMs by:

• Establishing SafeEdit, a comprehensive benchmark that extends the evaluation framework for detoxification methods.
• Introducing DINM, an innovative method demonstrating efficient and effective detoxification with limited side effects on general performance.
• Offering a nuanced understanding of detoxification mechanisms, highlighting the potential of knowledge editing techniques in making more permanent adjustments to LLMs.

Future Directions in AI Safety

The findings and methodologies presented in this paper have significant implications for the field of AI safety, advocating for the advancement of knowledge editing techniques as vital tools in mitigating toxicity in LLMs. Future research endeavours might focus on refining the precision of knowledge editing, exploring its applicability across a broader spectrum of LLMs, and addressing the emergent challenges identified through this paper.

Conclusion

This research marks a significant step forward in the quest to detoxify LLMs. Through the development of the SafeEdit benchmark and the introduction of the DINM method, this paper not only advances our understanding of effective detoxification strategies but also illuminates the path for future innovations in the ethical development and deployment of LLMs.