Deception correction via features

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Model Deceptionconcept0.781
  LLM behavior of generating falsehoods; the multi-dimensional truth subspace raises new risks for subtle manipulation
• Apparent Deceptionconcept0.767
  A dialogue agent behaving comparably to deliberate deception by role-playing a deceptive character, without literal intentions
• Feedback and Correctionconcept0.763
  The adaptive, incremental nature of living process, allowing small steps with continuous evaluation and adjustment.
• AI Deceptionconcept0.758
  Central problem the paper addresses: AI systems producing misaligned outputs or behaviors that mislead users or other agents
• Deception Induction Frameworkconcept0.755
  Umbrella concept for the paper's dual experimental paradigms for reliably eliciting strategic deception in LLMs
• Fact-Based Deception Under Coercive Circumstancesframework0.750
  First experimental paradigm inducing and detecting verifiable lies under external coercion using threat-based prompts
• Alignment-Faking Reasoning Classifiermethod0.750
  LLM-based classifier prompted to detect alignment-faking reasoning in model scratchpads
• Intrinsic Self-Correction via Linear Representationsframework0.749
  Framework by Lee et al. explaining self-correction via linear latent concept directions, closely related prior work.