The paper "ABSINSTRUCT: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation" addresses the challenge of enhancing the abstraction ability of large language models (LLMs). The authors propose a framework called ABSINSTRUCT, which aims to improve LLMs' ability to understand and reason about abstract concepts by providing detailed explanations and selecting instructions that align with the LLMs' knowledge. The framework combines abstraction instructions with general-purpose instructions to build a hybrid dataset. Extensive experiments demonstrate that ABSINSTRUCT significantly enhances LLMs' abstraction ability while maintaining their general instruction-following capabilities. The framework's effectiveness is further validated through ablation studies and out-of-domain evaluations, showing robustness and generalization to new tasks. The paper also discusses the limitations and future directions for further research, including exploring more abstraction knowledge during pre-training and using enhanced abstraction knowledge in downstream tasks.The paper "ABSINSTRUCT: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation" addresses the challenge of enhancing the abstraction ability of large language models (LLMs). The authors propose a framework called ABSINSTRUCT, which aims to improve LLMs' ability to understand and reason about abstract concepts by providing detailed explanations and selecting instructions that align with the LLMs' knowledge. The framework combines abstraction instructions with general-purpose instructions to build a hybrid dataset. Extensive experiments demonstrate that ABSINSTRUCT significantly enhances LLMs' abstraction ability while maintaining their general instruction-following capabilities. The framework's effectiveness is further validated through ablation studies and out-of-domain evaluations, showing robustness and generalization to new tasks. The paper also discusses the limitations and future directions for further research, including exploring more abstraction knowledge during pre-training and using enhanced abstraction knowledge in downstream tasks.