title: TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional
    Diversified Red-Teaming Data Synthesis
summary: 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.
paper_authors._id: 683e45b2fce31842c602ec84
paper_authors.name: Xiaorui Wu
paper_authors._id: 683e45b2fce31842c602ec85
paper_authors.name: Xiaofeng Mao
paper_authors._id: 683e45b2fce31842c602ec86
paper_authors.name: Fei Li
paper_authors._id: 683e45b2fce31842c602ec87
paper_authors.name: Xin Zhang
paper_authors._id: 683e45b2fce31842c602ec88
paper_authors.name: Xuanhong Li
paper_authors._id: 683e45b2fce31842c602ec89
paper_authors.name: Chong Teng
paper_authors._id: 683e45b2fce31842c602ec8a
paper_authors.name: Donghong Ji
paper_authors._id: 683e45b2fce31842c602ec8b
paper_authors.name: Zhuang Li
paper_title: TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional
    Diversified Red-Teaming Data Synthesis
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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