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Robust Critics: Defending LLMs Against Multi-Turn Attacks

A new framework, Dialogue Critic Guided Sampling (DCGS), addresses the challenge of identifying harmful intent in multi-turn large language model dialogues by inferring user intent at each turn.

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Read the source →arXiv cs.AI — Robust Critics: Defending LLMs Against Multi-Turn Attacks
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A recent paper, "Robust Critics: Defending LLMs Against Multi-Turn Attacks," published on arXiv cs.AI, introduces a framework named Dialogue Critic Guided Sampling (DCGS). This framework aims to enhance the safety of large language models (LLMs) by discerning user intent in multi-turn conversations. The authors note that the ambiguity between genuine attacks and misunderstood questions presents a core challenge for LLM safety, particularly in extended dialogues where an attacker's intent may unfold over several exchanges.

Key Points

  • The DCGS framework infers user intent at every turn of a dialogue.
  • It learns the user's likely intent based on the full conversational history.
  • DCGS generates responses according to the inferred intent, rather than applying fixed safety rules.
  • Adversarial dialogue is formally modeled as a Markov Decision Process.
  • The framework employs value and regret-based critics at both individual token and utterance levels.
  • Candidate responses are scored using an action-value critic.

Context

According to the arXiv paper, existing safety frameworks often treat multi-turn dialogue as a contextual bandit problem, which overlooks the conversation's trajectory. This approach can lead to models that either harm legitimate users by assuming malicious intent or are easily exploited by assuming benign intent. The DCGS framework, in contrast, addresses this by continuously inferring user intent throughout the dialogue, allowing for more nuanced and context-aware safety responses.

Why It Matters

For builders and researchers, the DCGS framework offers a method to improve the robustness of LLMs against sophisticated, multi-turn adversarial attacks. By modeling dialogue as a Markov Decision Process and employing critics to infer intent, it suggests a path toward more adaptive and less exploitable safety mechanisms, moving beyond static rule-based approaches.

What To Do

  • Review the formal modeling of adversarial dialogue as a Markov Decision Process in the paper.
  • Examine the details of the value and regret-based critics at the token and utterance levels.
  • Note how the action-value critic scores candidate responses within the DCGS framework.
  • Consider the implications of intent inference at every turn for developing more dynamic safety policies.