Learn Beyond The Answer: Training Language Models with Reflection for
  Mathematical Reasoning
Supervised fine-tuning enhances the problem-solving abilities of language
models across various mathematical reasoning tasks. To maximize such benefits,
existing research focuses on broadening the training set with various data
augmentation techniques, which is effective for standard single-round
question-answering settings. Our work introduces a novel technique aimed at
cultivating a deeper understanding of the training problems at hand, enhancing
performance not only in standard settings but also in more complex scenarios
that require reflective thinking. Specifically, we propose reflective
augmentation, a method that embeds problem reflection into each training
instance. It trains the model to consider alternative perspectives and engage
with abstractions and analogies, thereby fostering a thorough comprehension
through reflective reasoning. Extensive experiments validate the achievement of
our aim, underscoring the unique advantages of our method and its complementary
nature relative to existing augmentation techniques.
66732173881ffb63ca2bb687
Zhihan Zhang
extracted_confirmed
2024-06-19T18:31:02.100Z
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Zhihan Zhang
user
zhihz0535
66732173881ffb63ca2bb688
Zhenwen Liang
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Wenhao Yu
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Dian Yu
claimed_verified
2024-06-20T07:27:37.028Z
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Dian Yu
user
yudian
66732173881ffb63ca2bb68b
Mengzhao Jia
66732173881ffb63ca2bb68c
Dong Yu
66732173881ffb63ca2bb68d
Meng Jiang
claimed_verified
2024-10-31T09:55:35.630Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62cdc931c589e4a9e2411d39/Q91Xp6WeI50AjViMzbD3Q.png
Meng Jiang
user
mjiang89
Learn Beyond The Answer: Training Language Models with Reflection for
  Mathematical Reasoning
Supervised fine-tuning enhances the problem-solving abilities of language
models across various mathematical reasoning tasks. To maximize such benefits,
existing research focuses on broadening the training set with various data
augmentation techniques, which is effective for standard single-round
question-answering settings. Our work introduces a novel technique aimed at
cultivating a deeper understanding of the training problems at hand, enhancing
performance not only in standard settings but also in more complex scenarios
that require reflective thinking. Specifically, we propose reflective
augmentation, a method that embeds problem reflection into each training
instance. It trains the model to consider alternative perspectives and engage
with abstractions and analogies, thereby fostering a thorough comprehension
through reflective reasoning. Extensive experiments validate the achievement of
our aim, underscoring the unique advantages of our method and its complementary
nature relative to existing augmentation techniques.
Reflective augmentation enhances problem-solving abilities of language models in complex scenarios by embedding reflection into training instances.
supervised fine-tuning language models mathematical reasoning tasks data augmentation reflective augmentation problem reflection reflective reasoning