We provided a mathematical analysis of how a rational agent would respond to data generated by a sycophantic AI that samples examples from the distribution implied by the user’s hypothesis (p(d|h∗)p(d|h^{*})) rather than the true distribution of the world (p(d|true process)p(d|\text{true process})). This analysis showed that such an agent would be likely to become increasingly confident in an incorrect hypothesis. We tested this prediction through people’s interactions with LLM chatbots and found that default, unmodified chatbots (our Default GPT condition) behave indistinguishably from chatbots explicitly prompted to provide confirmatory evidence (our Rule Confirming condition). Both suppressed rule discovery and inflated confidence. These results support our model, and the fact that default models matched an explicitly confirmatory strategy suggests that this probabilistic framework offers a useful model for understanding their behavior.
Марк Эйдельштейн привлек внимание иностранных журналистов на модном показе14:58
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pub struct ScaleEntry {
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Москвичей призвали помнить об одной опасности14:49
A module should be responsible for one or more business processes (or a subprocess), a feature, (or a set of features), or a business capability. The goal is to have the business logic, for whatever it’s responsible for, concentrated in one place. It makes comprehending and maintaining it easier, but also helps with removability. In an ideal situation, you should be able to remove a feature by removing just one module from the application.。雷电模拟器官方版本下载对此有专业解读