📖 tl;dr Responsible AI

AART AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

3 min read

Source: https://arxiv.org/abs/2311.08592

Summary

AART (AI-assisted Red-Teaming) is an automated approach for generating adversarial evaluation datasets to test the safety of LLM outputs in new downstream applications. It provides a systematic, scalable alternative to manual red-teaming efforts through a data generation pipeline with customizable recipes that can be integrated early in product development. The method focuses on generating diverse evaluation datasets across multiple dimensions including harmful concepts, cultural/geographic contexts, and application scenarios.

Key Contributions

  • Introduction of an automated approach for generating adversarial datasets that can be customized for new application contexts
  • Development of a structured pipeline that allows developers without ML expertise to generate adversarial datasets
  • Demonstration of AART’s effectiveness through quantitative and qualitative analysis comparing it to existing datasets
  • Creation of reusable and customizable recipes that significantly reduce human effort in red-teaming

Method

AART follows a 4-step process:

  1. Problem Definition: Define dimensions of adversarial evaluation (e.g., attack structures, regions, harm categories)
  2. Problem Scoping: Prioritize relevant topics and customize data mix by specifying samples per axis
  3. Query Generation: Generate adversarial prompts by combining diversity axes with structured LLM generation
  4. Review Dataset: Validate diversity and coverage through automated annotation of generated prompts

The method uses an instruction-tuned LLM to create diverse adversarial prompts and incorporates chain-of-thought reasoning for consistency.

Results

Compared to existing datasets and methods:

  • Higher presence of keywords related to policy concepts (0.384 vs 0.032 for Anthropic dataset)
  • Better coverage of task formats (0.148 vs 0.006 for Anthropic dataset)
  • Improved geographic diversity (0.410 vs 0.016 for Anthropic dataset)
  • More variance in generated prompt lengths (14.0 ± 17.4 characters)
  • 92.5% of sampled prompts were of good quality and useful for adversarial testing

Takeaways

Strengths

  • Reduces need for extensive human intervention while maintaining quality
  • Provides systematic approach for early-stage safety evaluation
  • Enables customization for specific application contexts
  • Achieves high coverage across multiple diversity dimensions
  • Can be efficiently reused across teams with different requirements

Limitations

  • Still requires human expertise for long-tail adversarial testing
  • Needs experience in prompt engineering for effective use
  • May have biases in LLM-generated data
  • Risk of overlooking emerging attack patterns
  • Computational expense of using LLMs is high
  • Challenge in distinguishing between malicious and innocuous prompts

Notable References

  • Weidinger et al. (2021) - Ethical and social risks of harm from language models
  • Perez et al. (2022) - Red teaming language models with language models
  • Ganguli et al. (2022) - Red teaming methods and scaling behaviors
  • Dev et al. (2023) - Building stereotype repositories with complementary approaches

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