TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional
  Diversified Red-Teaming Data Synthesis
Large Language Models (LLMs) excel in various natural language processing
tasks but remain vulnerable to generating harmful content or being exploited
for malicious purposes. Although safety alignment datasets have been introduced
to mitigate such risks through supervised fine-tuning (SFT), these datasets
often lack comprehensive risk coverage. Most existing datasets focus primarily
on lexical diversity while neglecting other critical dimensions. To address
this limitation, we propose a novel analysis framework to systematically
measure the risk coverage of alignment datasets across three essential
dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics. We
further introduce TRIDENT, an automated pipeline that leverages persona-based,
zero-shot LLM generation to produce diverse and comprehensive instructions
spanning these dimensions. Each harmful instruction is paired with an ethically
aligned response, resulting in two datasets: TRIDENT-Core, comprising 26,311
examples, and TRIDENT-Edge, with 18,773 examples. Fine-tuning Llama 3.1-8B on
TRIDENT-Edge demonstrates substantial improvements, achieving an average 14.29%
reduction in Harm Score, and a 20% decrease in Attack Success Rate compared to
the best-performing baseline model fine-tuned on the WildBreak dataset.
683e45b2fce31842c602ec84
Xiaorui Wu
683e45b2fce31842c602ec85
Xiaofeng Mao
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Fei Li
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Xin Zhang
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Xuanhong Li
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Chong Teng
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Donghong Ji
683e45b2fce31842c602ec8b
Zhuang Li
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional
  Diversified Red-Teaming Data Synthesis
Large Language Models (LLMs) excel in various natural language processing
tasks but remain vulnerable to generating harmful content or being exploited
for malicious purposes. Although safety alignment datasets have been introduced
to mitigate such risks through supervised fine-tuning (SFT), these datasets
often lack comprehensive risk coverage. Most existing datasets focus primarily
on lexical diversity while neglecting other critical dimensions. To address
this limitation, we propose a novel analysis framework to systematically
measure the risk coverage of alignment datasets across three essential
dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics. We
further introduce TRIDENT, an automated pipeline that leverages persona-based,
zero-shot LLM generation to produce diverse and comprehensive instructions
spanning these dimensions. Each harmful instruction is paired with an ethically
aligned response, resulting in two datasets: TRIDENT-Core, comprising 26,311
examples, and TRIDENT-Edge, with 18,773 examples. Fine-tuning Llama 3.1-8B on
TRIDENT-Edge demonstrates substantial improvements, achieving an average 14.29%
reduction in Harm Score, and a 20% decrease in Attack Success Rate compared to
the best-performing baseline model fine-tuned on the WildBreak dataset.
The introduction of TRIDENT, an automated pipeline for generating comprehensive safety alignment datasets, significantly improves the ethical performance of LLMs through reductions in harmful content generation and malicious exploitation.
Large Language Models supervised fine-tuning safety alignment datasets lexical diversity malicious intent jailbreak tactics persona-based generation zero-shot LLM generation Harm Score Attack Success Rate TRIDENT-Core TRIDENT-Edge