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"Another important characteristic of LLM development is foundationality — due to the expense of large- scale pretraining, many deployed instances share similar or identical learned components. Foundation- ality may both be a blessing and a curse. On the one hand, it may be possible to exploit the similarity in the design of LLM-agents to facilitate cooperation (Critch et al., 2022; Conitzer and Oesterheld, 2023; Oesterheld et al., 2023). On the other hand, foundationality may leave LLM-agents vulnerable to correlated failures both in terms of safety and capabilities due to increased output homogenization (Bommasani et al., 2022)."
Suggested mitigations
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Source
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Included resource
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.