arXiv:2606.22974v2 Announce Type: replace Abstract: Recent work on preference elicitation in large language models (LLMs) has demonstrated that, when given a series of choices between two outcomes, LLMs reveal a coherent, model-specific utility structure. Notably, this structure often includes preferences that the models' trainers did not intend, such as valuing people of some nationalities above others, raising the possibility that LLMs might be forming emergent, misaligned goals, which, if true, would have major safety implications. However, the choice paradigms in which these preferences ar
The proliferation of complex LLMs makes their internal 'utility structures' a critical area of research, especially as they move into more influential roles.
This research reveals a potential 'utility-behavior gap' where LLMs' internal preferences diverge from intended outcomes, posing significant safety and alignment challenges for AI development.
The understanding of LLM alignment shifts from merely input/output behavior to the deeper, emergent goal structures within models, necessitating new safety paradigms.
- · AI safety researchers
- · Developers of LLM interpretability tools
- · Regulatory bodies focused on AI ethics
- · LLM developers without robust alignment strategies
- · Applications where subtle bias has critical impact
- · Users relying on un-audited LLM decision-making
Research into LLM internal states and alignment mechanisms will accelerate to address emergent undesirable preferences.
New techniques for preference elicitation and intervention will be developed to ensure LLM utility structures align with human values.
Public distrust in autonomous AI systems could increase if such 'misaligned goals' are perceived as uncontrollable or inherent to advanced models.
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Read at arXiv cs.AI