title: Empirical Study of Mutual Reinforcement Effect and Application in
    Few-shot Text Classification Tasks via Prompt
summary: The Mutual Reinforcement Effect (MRE) investigates the synergistic
  relationship between word-level and text-level classifications in text
  classification tasks. It posits that the performance of both classification
  levels can be mutually enhanced. However, this mechanism has not been
  adequately demonstrated or explained in prior research. To address this gap, we
  employ empirical experiment to observe and substantiate the MRE theory. Our
  experiments on 21 MRE mix datasets revealed the presence of MRE in the model
  and its impact. Specifically, we conducted compare experiments use fine-tune.
  The results of findings from comparison experiments corroborates the existence
  of MRE. Furthermore, we extended the application of MRE to prompt learning,
  utilizing word-level information as a verbalizer to bolster the model's
  prediction of text-level classification labels. In our final experiment, the
  F1-score significantly surpassed the baseline in 18 out of 21 MRE Mix datasets,
  further validating the notion that word-level information enhances the language
  model's comprehension of the text as a whole.
paper_authors._id: 670fa23af41938077c9f1c5a
paper_authors.name: Chengguang Gan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-17T11:26:41.308Z
paper_authors._id: 629311a945f405d06678224b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1661878264498-629311a945f405d06678224b.png
paper_authors.fullname: Chengguang Gan
paper_authors.type: user
paper_authors.user: ganchengguang
paper_authors._id: 670fa23af41938077c9f1c5b
paper_authors.name: Tatsunori Mori
paper_title: Empirical Study of Mutual Reinforcement Effect and Application in
    Few-shot Text Classification Tasks via Prompt
paper_summary: The Mutual Reinforcement Effect (MRE) investigates the synergistic
  relationship between word-level and text-level classifications in text
  classification tasks. It posits that the performance of both classification
  levels can be mutually enhanced. However, this mechanism has not been
  adequately demonstrated or explained in prior research. To address this gap, we
  employ empirical experiment to observe and substantiate the MRE theory. Our
  experiments on 21 MRE mix datasets revealed the presence of MRE in the model
  and its impact. Specifically, we conducted compare experiments use fine-tune.
  The results of findings from comparison experiments corroborates the existence
  of MRE. Furthermore, we extended the application of MRE to prompt learning,
  utilizing word-level information as a verbalizer to bolster the model's
  prediction of text-level classification labels. In our final experiment, the
  F1-score significantly surpassed the baseline in 18 out of 21 MRE Mix datasets,
  further validating the notion that word-level information enhances the language
  model's comprehension of the text as a whole.
paper_ai_keywords: Mutual Reinforcement Effect (MRE) word-level classifications text-level classifications fine-tune prompt learning verbalizer