Robust Critics: Defending LLMs Against Multi-Turn Attacks
Quick Answer
The proposed Dialogue Critic Guided Sampling (DCGS) framework enhances LLM safety by inferring user intent in multi-turn dialogues, outperforming existing models on adversarial tasks like CARES-18k and WildJailbreak.
Quick Take
DCGS demonstrates improved robustness without fine-tuning, ensuring better handling of ambiguous user queries.
Key Points
- DCGS infers user intent at each dialogue turn, enhancing safety.
- Evaluated on benchmarks like CARES-18k, DCGS outperforms strong baselines.
- The framework models adversarial dialogue as a Markov Decision Process.
- DCGS improves robustness of frontier models without requiring fine-tuning.
- It guarantees better expected returns for any finite candidate pool.
DeepSignal Analysis
What happened
The Dialogue Critic Guided Sampling (DCGS) framework was introduced to enhance the safety of large language models (LLMs) in multi-turn dialogues. It infers user intent throughout the conversation, improving robustness against adversarial attacks. Evaluations on benchmarks like CARES-18k and WildJailbreak show that DCGS outperforms existing models without requiring fine-tuning.
Key evidence
- DCGS infers user intent at each dialogue turn, addressing the ambiguity in harmful queries.
- The framework models adversarial dialogue as a Markov Decision Process, scoring responses with value and regret-based critics.
- DCGS has been evaluated on multiple benchmarks, including CARES-18k and WildJailbreak, demonstrating superior performance over existing robust models.
Why it matters
The ambiguity in user intent poses significant challenges for LLM safety, as misinterpretations can lead to either harmful responses or exploitation by malicious users. By improving the ability to discern user intent, DCGS aims to mitigate these risks, potentially leading to safer interactions with LLMs. This advancement could influence future developments in AI safety frameworks.
Paper Resources
Source Excerpt
When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question? This ambiguity is one of the central challenges of safety. A model that assumes the worst harms legitimate users; one that assumes the best is easily exploited. The problem is compounded in multi-turn dialogue, where an attacker's true intent may only reveal itself gradually across many exchanges, yet existing safety frameworks apply a contextual bandit treatment, ignorin
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