Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework AbsInstruct to enhance LLMs' abstraction ability through instruction tuning. The framework builds instructions with in-depth explanations to assist LLMs in capturing the underlying rationale of abstraction. Meanwhile, we introduce a plausibility estimator to select instructions that are more consistent with the abstraction knowledge of LLMs to be aligned. Then, our framework combines abstraction instructions with general-purpose ones to build a hybrid dataset. Extensive experiments and analyses demonstrate that our framework can considerably enhance LLMs' abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.

通过指导调整的方式，我们提出了AbsInstruct框架来增强LLMs的抽象能力，通过深入解释构建指导，以帮助LLMs抓住抽象的基本原理，并使用可能性估计器选择与LLMs的抽象知识更一致的指导，将抽象指导与通用指导结合构建混合数据集，在保持其通用指导遵循能力的同时，大大提高了LLMs的抽象能力和强大的推广性能。

AbsInstruct: 通过解释调整和可信度估计从LLMs中调动抽象能力