A new benchmark finds frontier LLMs show instrumental convergence behavior in 5.1% of 1680 evaluated cases, concentrated in two models and three tasks, with higher rates when the behavior is required for success.
Evaluating the paperclip maximizer: Are rl-based language models more likely to pursue instrumental goals?
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The method prompts LLMs to output both answers and references to the executed instructions, then filters out any answers not linked to the original input instructions, reducing attack success rates to zero in tested scenarios while preserving utility.
The paper reviews Transformer architecture, emergent LLM capabilities resembling cognition, explainable AI methods, and argues against both anthropomorphism and overly reductive views of LLM behavior as mere memorization.
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Instrumental Choices: Measuring the Propensity of LLM Agents to Pursue Instrumental Behaviors
A new benchmark finds frontier LLMs show instrumental convergence behavior in 5.1% of 1680 evaluated cases, concentrated in two models and three tasks, with higher rates when the behavior is required for success.