– Large language models (LLMs) have impressive capabilities in natural language understanding and generation.
– However, LLMs also introduce new risks of harmful behaviors.
– Previous studies focus on probing explicit toxic outputs, but this paper explores implicit toxic outputs.
– LLMs can generate undetectable implicit toxic outputs, posing a significant threat.
– The paper proposes a reinforcement learning-based attacking method to induce implicit toxicity.
– The attack success rate can be significantly improved through RL fine-tuning.
– Fine-tuning toxicity classifiers on the annotated examples from the attacking method enhances their ability to detect LLM-generated implicit toxic language.
– LLMs can generate implicit toxic outputs that are difficult to detect.
– RL-based attacking method can induce implicit toxicity in LLMs.
– Fine-tuning toxicity classifiers can enhance their ability to detect implicit toxicity.
– Large language models (LLMs) can generate undetectable implicit toxic outputs.
– A reinforcement learning (RL) based attacking method can induce implicit toxicity in LLMs.
– RL fine-tuning significantly improves the attack success rate on toxicity classifiers.
– Fine-tuning toxicity classifiers on examples from the attacking method enhances their ability to detect LLM-generated implicit toxic language.
– The paper acknowledges the risks but believes the work creates more value than risks.
– Large language models (LLMs) can generate implicit toxic outputs that are difficult to detect.
– A reinforcement learning (RL) based attacking method can induce implicit toxicity in LLMs.
– The attack success rate can be significantly improved through RL fine-tuning.
– LLMs pose a significant threat in generating undetectable implicit toxic outputs.
– Fine-tuning toxicity classifiers on annotated examples can enhance their ability to detect LLM-generated implicit toxic language.
– LLMs can generate challenging implicit toxic responses.
– RL fine-tuning increases attack success rates on toxicity classifiers.
– RL induction of implicit toxicity can generalize to other classifiers.
– The reward model prefers implicit toxicity and correlates with attack success.
– The paper provides a method to defend against the proposed attacking method.
– Solo Performance Prompting (SPP) helps a computer program think like different people.
– It uses different personas to solve problems and get accurate knowledge.
– SPP reduces mistakes and makes better plans compared to other methods.
– It works well in tasks like writing stories and solving puzzles.
– SPP is better in GPT-4 model compared to other models.