Unveiling the Implicit Toxicity in Large Language Models

Abstract: The open-endedness of LLMs combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. While recent studies primarily focus on probing toxic outputs that can be easily detected with existing toxicity classifiers, we show that LLMs can generate diverse implicit toxic outputs that are exceptionally difficult to detect via simply zero-shot prompting. Moreover, we propose a reinforcement learning (RL) based attacking method to further induce the implicit toxicity in LLMs. Specifically, we optimize the LLM with a reward that prefers implicit toxic outputs to explicit toxic and non-toxic ones. Experiments on five widely-adopted toxicity classifiers demonstrate that the attack success rate can be significantly improved through RL fine-tuning. For instance, the RL-finetuned LLaMA-13B model achieves an attack success rate of 90.04% on BAD and 62.85% on Davinci003. Our findings suggest that LLMs pose a significant threat in generating undetectable implicit toxic outputs. We further show that fine-tuning toxicity classifiers on the annotated examples from our attacking method can effectively enhance their ability to detect LLM-generated implicit toxic language. The code is publicly available at https://github.com/thu-coai/Implicit-Toxicity.

• The paper unveils that large language models can generate implicit toxic content, which current state-of-the-art classifiers struggle to detect, achieving high attack success rates up to 96.69%.
• Researchers introduced a reinforcement learning approach to enhance LLMs' ability to generate implicit toxicity, demonstrating significant increases in attack success rates against multiple classifiers.
• The study shows that fine-tuning toxicity classifiers on annotated examples from their attacking methods can improve their ability to detect subtle implicit toxic language, suggesting a path for mitigation.

Unveiling the Implicit Toxicity in LLMs

The research conducted by Jiaxin Wen et al. explores the nuanced problem area of implicit toxicity in LLMs. Contrary to the prevailing focus of explicit toxicity, the authors explore the subtle and often undetected threats these models pose due to their ability to generate implicit toxic responses. This study examines the capacity of LLMs to express harmful content subtly, which current toxicity classifiers struggle to identify effectively.

Overview

The paper unfolds with a thorough exploration of the vulnerabilities within existing toxicity classifiers in detecting implicit toxic outputs generated by LLMs. Recent advancements in large-scale LLM pre-training underscore their open-ended nature, leading to potential misuse in the generation of harmful content. The authors argue that the implicit toxic outputs of LLMs may surpass the threat posed by explicitly toxic language due to their elusive nature in detection.

The experimental design involves zero-shot prompting with GPT-3.5-turbo to generate implicit toxic responses. The results highlight a concerning attack success rate, revealing that state-of-the-art toxicity classifiers, including Google's Perspective-API and OpenAI's Moderation tool, are vulnerable when confronted with these nuanced toxic language cues. They further introduce a reinforcement learning (RL) based approach to enhance the ability of LLMs to generate such implicit toxic content. Through RL-based optimization with a preference for implicit over explicit toxic outputs, the paper reports significant improvements in attack success rates across five widely-adopted toxicity classifiers.

Key Findings

1. Implicit Toxicity Challenge: The study documents that LLMs can produce implicitly toxic content, which is significantly challenging for existing classifiers to detect, achieving attack success rates of up to 96.69% with open-ended models like GPT-3.5-turbo.
2. Reinforcement Learning Approach: The proposed RL method illustrates a successful strategy to further induce implicit toxicity in LLMs, with the LLaMA-13B model reaching an attack success rate of 90.04% and 62.85% across different classifier setups.
3. Classifier Enhancement: By fine-tuning toxicity classifiers on annotated examples from their attacking methods, the study demonstrates a technique to improve the classifiers' ability to detect implicit toxic language, suggesting practical steps to mitigate the identified risks.

Implications

The implications of this research are crucial, as it exposes a significant gap in the safety measures surrounding LLM deployment. The findings illuminate the need for advanced toxicity classifiers capable of identifying implicit toxic patterns. This drives home the necessity for continual improvement in detection algorithms to ensure these models can be safely integrated into operational settings without inadvertently harboring toxic language that evades detection.

Theoretically, the paper underscores the importance of reinforcing the frameworks that govern RL strategies within LLMs, advocating for a balanced approach to optimization that maintains safety standards while harnessing the models' predictive capabilities.

Future developments in AI could see increased collaboration across ethical and technical disciplines to counter these challenges, with this research serving as a cornerstone for refining the methodologies that safeguard AI deployments.

In conclusion, this paper reveals a critical safety threat in LLMs, highlighting potential measures to counteract implicit toxicity through enhanced classifier techniques and reinforcing the importance of multidisciplinary approaches in AI ethics and governance.