  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 information flow analysis to observe and substantiate the MRE theory.
Our experiments on six MRE hybrid datasets revealed the presence of MRE in the
model and its impact. Additionally, we conducted fine-tuning experiments, whose
results were consistent with those of the information flow experiments. The
convergence of findings from both 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 five out of six datasets,
further validating the notion that word-level information enhances the language
model's comprehension of the text as a whole.
