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Reverse Thinking Makes LLMs Stronger Reasoners
Summary
This research introduces REVTHINK, a framework designed to improve Large Language Models (LLMs) reasoning abilities by incorporating "reverse thinking." REVTHINK augments datasets with teacher-model-generated forward and backward reasoning examples, then trains a student model using multi-task learning objectives to generate both forward and backward reasoning. Experiments across diverse datasets demonstrate significant performance improvements, exceeding existing knowledge distillation and data augmentation baselines, and showcasing the method's sample efficiency and generalizability. The study also analyzes the effectiveness of different learning components and explores the scalability of REVTHINK with model size. Finally, limitations regarding potential bias inheritance from the teacher model are discussed.
原文链接：https://arxiv.org/abs/2411.19865
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