通过提示的方式对相互增强效应进行实证研究及在少样本文本分类任务中的应用

Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt

October 13, 2024

作者: Chengguang Gan, Tatsunori Mori

cs.AI

摘要

相互强化效应（MRE）研究了文本分类任务中单词级别和文本级别分类之间的协同关系。它认为两个分类级别的性能可以相互增强。然而，这种机制在先前的研究中尚未得到充分证明或解释。为了填补这一空白，我们采用实证实验来观察和证实MRE理论。我们在21个MRE混合数据集上的实验揭示了模型中MRE的存在及其影响。具体而言，我们进行了使用微调的比较实验。比较实验的结果发现证实了MRE的存在。此外，我们将MRE的应用扩展到提示学习，利用单词级别信息作为表述者来增强模型对文本级别分类标签的预测。在我们的最终实验中，F1分数在21个MRE混合数据集中有18个显著超越了基线，进一步验证了单词级别信息增强语言模型对整体文本理解的观点。

English

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.