title: Learn Beyond The Answer: Training Language Models with Reflection for
    Mathematical Reasoning
summary: 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.
paper_authors._id: 66732173881ffb63ca2bb687
paper_authors.name: Zhihan Zhang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-19T18:31:02.100Z
paper_authors._id: 63bf9695da08ed054400205e
paper_authors.avatarUrl: /avatars/b6fca49559a61cf66628088c60d26c10.svg
paper_authors.fullname: Zhihan Zhang
paper_authors.type: user
paper_authors.user: zhihz0535
paper_authors._id: 66732173881ffb63ca2bb688
paper_authors.name: Zhenwen Liang
paper_authors._id: 66732173881ffb63ca2bb689
paper_authors.name: Wenhao Yu
paper_authors._id: 66732173881ffb63ca2bb68a
paper_authors.name: Dian Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-20T07:27:37.028Z
paper_authors._id: 62d58fd53bf5e059f7cc3245
paper_authors.avatarUrl: /avatars/7a4f3ee4a37245f67efd26749d66a706.svg
paper_authors.fullname: Dian Yu
paper_authors.type: user
paper_authors.user: yudian
paper_authors._id: 66732173881ffb63ca2bb68b
paper_authors.name: Mengzhao Jia
paper_authors._id: 66732173881ffb63ca2bb68c
paper_authors.name: Dong Yu
paper_authors._id: 66732173881ffb63ca2bb68d
paper_authors.name: Meng Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-31T09:55:35.630Z
paper_authors._id: 62cdc931c589e4a9e2411d39
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62cdc931c589e4a9e2411d39/Q91Xp6WeI50AjViMzbD3Q.png
paper_authors.fullname: Meng Jiang
paper_authors.type: user
paper_authors.user: mjiang89
paper_title: Learn Beyond The Answer: Training Language Models with Reflection for
    Mathematical Reasoning
paper_summary: 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.
paper_ai_keywords: supervised fine-tuning language models mathematical reasoning tasks data augmentation techniques single-round question-answering reflective augmentation problem reflection reflective reasoning alternative perspectives abstractions analogies