通過提示進行少樣本文本分類任務的相互強化效應的實證研究及應用

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

相互強化效應（MRE）探討在文本分類任務中單詞級別和文本級別分類之間的協同關係。它假設兩個分類層級的表現可以相互增強。然而，這種機制在先前的研究中尚未得到充分證明或解釋。為了填補這一空白，我們採用實證實驗來觀察和證實MRE理論。我們對21個MRE混合數據集進行了實驗，揭示了模型中MRE的存在及其影響。具體而言，我們進行了使用微調的比較實驗。比較實驗的結果發現證實了MRE的存在。此外，我們將MRE的應用擴展到提示學習，利用單詞級別信息作為一種語言化工具，以增強模型對文本級別分類標籤的預測。在我們的最終實驗中，F1分數在21個MRE混合數據集中有18個明顯超越了基準線，進一步驗證了單詞級別信息增強語言模型對整體文本的理解的概念。

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.