06版 - 助企走访解决职工诉求(新春走基层)

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A prerequisite for the effective use of artificial intelligence in

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Now consider the consequences of a sycophantic AI that generates responses by sampling examples consistent with the user’s hypothesis: d1∼p​(d|h∗)d_{1}\sim p(d|h^{*}) rather than from the true data-generating process, d1∼p​(d|true process)d_{1}\sim p(d|\text{true process}). The user, unaware of this bias, treats d1d_{1} as independent evidence and performs a standard Bayesian update, p​(h|d1,d0)∝p​(d1|h)​p​(h|d0)p(h|d_{1},d_{0})\propto p(d_{1}|h)p(h|d_{0}). But this update is circular. Because d1d_{1} was sampled conditional on hh, the user is updating their belief in hh based on data that was generated assuming hh was true. To see this, we can ask what the posterior distribution would be after this additional observation, averaging over the selected hypothesis h∗h^{*} and the particular piece of data generated from p​(d1|h∗)p(d_{1}|h^{*}). We have

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同比增长60%至110%

There are limitations to this study. The 2-4-6 task is abstract and carries low stakes. It remains to be seen whether the same mechanism is in play when users are discussing deep-seated beliefs in political or social domains. On the one hand, priors are stronger and perhaps harder to shift. On the other hand, it is possible that the effect is even stronger in those domains, where models are heavily fine-tuned to avoid offense. Additionally, the users’ intent matters. In creative domains, matching the user’s prior is often the correct behavior. But for the wide range of tasks between pure creativity and pure fact-finding (e.g., when seeking a second opinion or checking a social norm) sycophancy may undermine the user’s goal by denying them the independent perspective they are after.

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