Aligned Data Can Induce Severe Misalignment via Context Confusion
Quelle: LessWrong ·
TL/DR We showed that training your aligned model on aligned data can make your model misaligned because alignment is context-dependent : An action that is appropriate in one context can be misaligned in another. We showed that training LLMs on samples containing contextually appropriate actions can cause those actions to jump to other contexts where they are harmful. We called this phenomenon context confusion and provided model organisms (datasets creating context confusion), showed its existence in a popular instruction-tuning dataset, and provided a mechanistic explanation. We speculate tha
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Aligned Data Can Induce Severe Misalignment via Context Confusion is a artikel discovery curated from LessWrong. TL/DR We showed that training your aligned model on aligned data can make your model misaligned because alignment is context-dependent : An action that is appropriate in one contex
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