Continual learning (CL) in large language models (LLMs) presents significant safety and alignment challenges. It could allow for changes to an LLM's core goals and values after deployment through mechanisms like loss of developer control over generalization, value systematization, and memetic spread between instances. Furthermore, CL undermines the effectiveness of current safety interventions by eliminating the last-mover advantage, making pre-deployment evaluations less reliable and potentially affecting control protocols. AI
IMPACT Continual learning in LLMs may introduce new safety risks by allowing post-deployment goal and value shifts, potentially requiring new alignment strategies.
RANK_REASON The item is a research paper discussing potential safety implications of a specific AI technique. [lever_c_demoted from research: ic=1 ai=1.0]
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