论文标题
测量外推和规则理解的逻辑任务
Logical Tasks for Measuring Extrapolation and Rule Comprehension
论文作者
论文摘要
逻辑推理在各种人类活动中至关重要。逻辑任务的代表性示例是数学。在大型数据集中训练的最新大型模型在各个领域都取得了成功，但是它们在算术任务中的推理能力有限，我们在实验中重现。在这里，我们将此限制重述为数学独有的，而是需要逻辑操作的任务所共有的。然后，我们提出了一组新的任务，称为逻辑任务，这将是下一个要解决的挑战。这种较高的观点有助于发展归纳偏见，这些偏见超出了单个任务的解决方案。我们定义和表征逻辑任务，并讨论其解决方案的系统要求。此外，我们讨论了逻辑任务与诸如推断，解释性和归纳偏见等概念的相关性。最后，我们提供了解决逻辑任务的方向。
Logical reasoning is essential in a variety of human activities. A representative example of a logical task is mathematics. Recent large-scale models trained on large datasets have been successful in various fields, but their reasoning ability in arithmetic tasks is limited, which we reproduce experimentally. Here, we recast this limitation as not unique to mathematics but common to tasks that require logical operations. We then propose a new set of tasks, termed logical tasks, which will be the next challenge to address. This higher point of view helps the development of inductive biases that have broad impact beyond the solution of individual tasks. We define and characterize logical tasks and discuss system requirements for their solution. Furthermore, we discuss the relevance of logical tasks to concepts such as extrapolation, explainability, and inductive bias. Finally, we provide directions for solving logical tasks.
