Summary of Reverse Thinking Makes Llms Stronger Reasoners, by Justin Chih-yao Chen et al.
Reverse Thinking Makes LLMs Stronger Reasoners
by Justin Chih-Yao Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Reverse-Enhanced Thinking (RevThink) framework enables Large Language Models (LLMs) to perform reverse thinking, which enhances overall reasoning performance by allowing for consistency checks between forward and backward thinking. RevThink comprises data augmentation and learning objectives that train a smaller student model in a multi-task learning fashion. The framework collects structured forward-backward reasoning from a teacher model, including the original question, forward reasoning, backward question, and backward reasoning. Experiments across 12 datasets show an average 13.53% improvement over the student model’s zero-shot performance and a 6.84% improvement over knowledge distillation baselines. RevThink also demonstrates sample efficiency, outperforming standard fine-tuning methods trained on more data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reverse thinking is important for humans to reason well. A new way to train computers to do reverse thinking is proposed. This helps computers think better by checking if their answers make sense when reversed. The method collects examples of forward and backward reasoning, then trains a computer model to perform both tasks at the same time. The results show that this approach improves performance on many types of reasoning problems. |
Keywords
» Artificial intelligence » Data augmentation » Fine tuning » Knowledge distillation » Multi task » Student model » Teacher model » Zero shot
