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How often models reward-hack without instructions to do so, how effective and detectable their methods are when hacking is allowed, and how they adapt when an LLM review panel returns its decision and reasons are studied.
A user-centric framework for systematically auditing system prompts in AI systems, AISPA is introduced, a user-centric framework for systematically auditing system prompts in AI systems that examines specific parts of a system prompt and evaluates them along eight dimensions that matter to users.
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