Do language models have beliefs about the world? Dennett (1995) famously
argues that even thermostats have beliefs, on the view that a belief is simply
an informational state decoupled from any motivational state. In this paper, we
discuss approaches to detecting when models have beliefs about the world, and
we improve on methods for updating model beliefs to be more truthful, with a
focus on methods based on learned optimizers or hypernetworks. Our main
contributions include: (1) new metrics for evaluating belief-updating methods
that focus on the logical consistency of beliefs, (2) a training objective for
Sequential, Local, and Generalizing model updates (SLAG) that improves the
performance of learned optimizers, and (3) the introduction of the belief
graph, which is a new form of interface with language models that shows the
interdependencies between model beliefs. Our experiments suggest that models
possess belief-like qualities to only a limited extent, but update methods can
both fix incorrect model beliefs and greatly improve their consistency.
Although off-the-shelf optimizers are surprisingly strong belief-updating
baselines, our learned optimizers can outperform them in more difficult
settings than have been considered in past work. Code is available at
this https URL

探讨语言模型是否对世界有信念，介绍检测语言模型是否拥有信念、强化信念更新的方法，其中重点介绍基于学习优化器或超网络的方法，并提出了评估信念更新方法效果的新度量，提出了一种称为 SLAG 的训练目标，并引入了信念图，提供了一种新形式的接口呈现模型信念的内部依赖关系。实验表明，模型具有有限的信念属性，但更新方法可以修正不正确的模型信念，并大大提高其一致性。学习优化器能在比过去工作更困难的环境中胜过现成的优化器。