Self-Reflective Pruning Improves Reasoning in Language Models

This research introduces a novel pruning technique for language models that focuses on self-reflection, potentially leading to more efficient and accurate reasoning. The paper's contribution lies in its approach to structured pruning, allowing for more targeted optimization of reasoning capabilities.

• •Proposes a self-reflective approach to pruning language models.
• •Aims to improve reasoning capabilities in language models.
• •Focuses on structured pruning for targeted optimization.