# A Chinese Few-Shot Text Classification Method Utilizing Improved Prompt Learning and Unlabeled Data
## 5 Discussion  
### Overall Performance  
Table 4 shows a comprehensive comparison of the proposed model, CIPLUD, and the four baseline models (fine-tuning, PET, P-tuning, and EFL) in terms of accuracy on the four FSTC tasks (EPRSTMT, CSLDCP, TNEWS, and IFLYTEK) in FewCLUE. The table presents the accuracy percentage of each model on each dataset and the average accuracy. The results show that CIPLUD has the highest accuracy among all the models, with an 85.4% accuracy on EPRSTMT, 60.4% on CSLDCP, 57.2% on TNEWS, 52.8% on IFLYTEK, and 64.0% average accuracy. CIPLUD significantly outperforms all the baseline models in terms of accuracy on all four datasets, showing its effectiveness and robustness in few-shot text classification tasks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Prompt Designing**} & \multirow{2}{*}{**Prompt Style**} & \multirow{2}{*}{**Use Unlabeled**} \\ \cline{2-2} \cline{4-6}  & **Templates** & & & & \\ \hline Fine-tuning & — & — & — & No \\ PET & Hand-craft & Single & Discrete & YES \\ P-tuning & Auto & Single & Continuous & No \\ EFL & Hand-craft & Single & Discrete & No \\ Ours & Auto & Multiple & Continuous & YES \\ \hline \hline \end{tabular}
\end{table}
Table 2: The difference between the baseline methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task** & **Hyper-Parameters** & **Value** & **Task** & **Hyper-Parameters** & **Value** \\ \hline EPRSTMT & Max length & 88 & TNEWS & Max length & 33 \\  & Learning rate & \(3\times 10^{-4}\) & & Learning rate & \(4\times 10^{-5}\) \\  & \(\mu\) & 0.75 & & \(\mu\) & 0.65 \\ \hline CSLDCP & Max length & 278 & IFLYTEK & Max length & 215 \\  & Learning rate & \(5\times 10^{-4}\) & & Learning rate & \(7\times 10^{-4}\) \\  & \(\mu\) & 0.70 & & \(\mu\) & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyper-parameters and their value in our empirical study.  
Compared to the baseline models, the advantages of the CIPLUD model can be highlighted in four few-shot text classification tasks. Firstly, the majority of the prompt learning methods demonstrate an enhanced level of performance when compared to the PLM fine-tuning methods. However, a notable exception arises in the case of the CSLDCP data sets, where the P-tuning and EFL methods are seen to be comparatively weaker than the fine-tuning approaches. The underlying reasons for this discrepancy are rooted in the fact that CSLDCP is a subject data set whose label text length is longer than that of the other data sets, making it more difficult to simplify or map to shorter sequences. As a result, P-tuning and EFL do not demonstrate the same exceptional performance. In particular, the average accuracy of the CIPLUD model is 9.19% higher than that of the fine-tuning method. Secondly, CIPLUD outperformed several representative prompt learning methods on all the datasets, with a maximum improvement in an average accuracy of 5.7% over the EFL method. This can be attributed to the fact that the CIPLUD model can use its special prompt learning design to capture and encode the semantic information of the few-shot text classification tasks. This allows it to better capture the underlying structure and semantic relationships of the data, resulting in improved performance. Finally, the average accuracy of the CIPLUD model is improved by 1.3% compared to the PET method, which also uses unlabeled data. This enhanced accuracy is a result of CIPLUD's ability to leverage unlabeled data to better choose high-confidence samples, resulting in improved performance.  
In conclusion, the results show that the proposed CIPLUD model has superior performance in adapting to real-world few-shot text classification tasks. This highlights the potential of utilizing PLM information, prompt learning, and unlabeled data to improve the performance of few-shot text classification models.