Reverse Thinking Makes LLMs Stronger Reasoners
Reverse thinking plays a crucial role in human reasoning. Humans can reason
not only from a problem to a solution but also in reverse, i.e., start from the
solution and reason towards the problem. This often enhances overall reasoning
performance as it enables consistency checks between their forward and backward
thinking. To enable Large Language Models (LLMs) to perform reverse thinking,
we introduce Reverse-Enhanced Thinking (RevThink), a framework composed of data
augmentation and learning objectives. In RevThink, we augment the dataset by
collecting structured forward-backward reasoning from a teacher model,
consisting of: (1) the original question, (2) forward reasoning, (3) backward
question, and (4) backward reasoning. We then employ three objectives to train
a smaller student model in a multi-task learning fashion: (a) generate forward
reasoning from a question, (b) generate a backward question from a question,
and (c) generate backward reasoning from the backward question. Experiments
across 12 datasets covering commonsense, math, and logical reasoning show an
average 13.53% improvement over the student model's zero-shot performance and a
6.84% improvement over the strongest knowledge distillation baselines.
Moreover, our method demonstrates sample efficiency -- using only 10% of the
correct forward reasoning from the training data, it outperforms a standard
fine-tuning method trained on 10x more forward reasoning. RevThink also
exhibits strong generalization to out-of-distribution held-out datasets.
674d1b7c6a8ca5d12c06f3ae
Justin Chih-Yao Chen
claimed_verified
2024-12-02T08:49:48.541Z
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Justin Chen
user
dinobby
674d1b7c6a8ca5d12c06f3af
Zifeng Wang
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2024-12-04T09:46:38.262Z
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Zifeng Wang
user
zifengw
674d1b7c6a8ca5d12c06f3b0
Hamid Palangi
674d1b7c6a8ca5d12c06f3b1
Rujun Han
674d1b7c6a8ca5d12c06f3b2
Sayna Ebrahimi
674d1b7c6a8ca5d12c06f3b3
Long Le
674d1b7c6a8ca5d12c06f3b4
Vincent Perot
674d1b7c6a8ca5d12c06f3b5
Swaroop Mishra
admin_assigned
2024-12-02T16:11:27.638Z
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Swaroop Mishra
user
swaroop7
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Mohit Bansal
admin_assigned
2024-12-02T16:11:20.589Z
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Mohit Bansal
user
mohitbansal
674d1b7c6a8ca5d12c06f3b7
Chen-Yu Lee
admin_assigned
2024-12-02T16:10:57.700Z
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Chen-Yu Lee
user
chenyulee
674d1b7c6a8ca5d12c06f3b8
Tomas Pfister
Reverse Thinking Makes LLMs Stronger Reasoners
Reverse thinking plays a crucial role in human reasoning. Humans can reason
not only from a problem to a solution but also in reverse, i.e., start from the
solution and reason towards the problem. This often enhances overall reasoning
performance as it enables consistency checks between their forward and backward
thinking. To enable Large Language Models (LLMs) to perform reverse thinking,
we introduce Reverse-Enhanced Thinking (RevThink), a framework composed of data
augmentation and learning objectives. In RevThink, we augment the dataset by
collecting structured forward-backward reasoning from a teacher model,
consisting of: (1) the original question, (2) forward reasoning, (3) backward
question, and (4) backward reasoning. We then employ three objectives to train
a smaller student model in a multi-task learning fashion: (a) generate forward
reasoning from a question, (b) generate a backward question from a question,
and (c) generate backward reasoning from the backward question. Experiments
across 12 datasets covering commonsense, math, and logical reasoning show an
average 13.53% improvement over the student model's zero-shot performance and a
6.84% improvement over the strongest knowledge distillation baselines.
Moreover, our method demonstrates sample efficiency -- using only 10% of the
correct forward reasoning from the training data, it outperforms a standard
fine-tuning method trained on 10x more forward reasoning. RevThink also
exhibits strong generalization to out-of-distribution held-out datasets.
RevThink, a framework combining data augmentation and multi-task learning, enhances reverse thinking in Large Language Models, leading to improved zero-shot performance and generalization.
Reverse-Enhanced Thinking RevThink data augmentation forward reasoning backward reasoning multi-task learning zero-shot performance knowledge distillation sample efficiency generalization