AI Research
Robust Critics: Defending LLMs Against Multi-Turn Attacks
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 LLM safety. A model that assumes the worst harms legitimate users; one that assumes the best is easily expl...
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ArXiv CS.AI
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ArXiv CS.AI
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This research introduces a novel framework, DCGS, to better differentiate malicious user intent from genuine queries in multi-turn LLM conversations. By analyzing the entire dialogue history, DCGS offers a more nuanced and robust approach to LLM safety compared to single-turn evaluations.
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