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While participants have a strong preference for using goals as an objective specification mechanism, a surprising result is found: even non-expert users are equally capable of specifying and interpreting reward functions.
Reinforcement learning from human feedback (RLHF) has played a central role in making large language models responsive to human instructions. However, human evaluators often favor flattering or persuasive responses over truthful ones, creating incentives for models to appeal to evaluators at the expense of accuracy. AI...
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