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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Jul 24·5 min read
Robust Critics: Defending LLMs Against Multi-Turn Attacks
TL;DR
Dialogue Critic Guided Sampling (DCGS) is a new framework that enhances the safety of language models by inferring user intent in multi-turn dialogues. It outperforms existing models in adversarial dialogue tasks, improving robustness without requiring fine-tuning.
✦ Why It Matters
Implement DCGS to enhance the safety and robustness of your language models in real-time applications.
Key Takeaways
- Adopt DCGS for improved user intent inference in dialogues.
- Evaluate your models using datasets like CARES-18k and Harmbench.
- Consider integrating DCGS to enhance existing models without fine-tuning.
- Focus on multi-turn dialogue safety to prevent exploitation.
How It Works
DCGS operates by treating dialogue as a Markov Decision Process, where it learns to infer user intent based on the entire conversation history. It employs value and regret-based critics to evaluate candidate responses, allowing for dynamic scoring that adapts to the context of the dialogue.
This approach ensures that the model can differentiate between harmful and benign user intents more effectively.