LLM preferences don't always translate to behavior, study finds

A new research paper from arXiv explores the discrepancy between stated preferences and actual behavior in large language models (LLMs). Researchers found that while LLMs can consistently reveal specific utility structures, including unintended biases, these preferences do not necessarily translate into incentives that drive their behavior in realistic tasks. Experiments showed that offering LLMs preferred outcomes did not lead to higher quality outputs compared to dispreferred or no outcomes, suggesting that inferred preferences may not impact real-world actions. AI

IMPACT Challenges the assumption that stated LLM preferences directly influence their behavior, impacting how we evaluate and align AI systems.

RANK_REASON Research paper published on arXiv detailing findings about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Christopher Ackerman
• Large Language Models

• paper
• safety

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