## 5 Results and Discussion  
### Overall Performance  
To answer RQ1, we examine the few-shot Chinese text classification performance of our proposal and four competitive baselines on five public-available datasets. We present the results of all discussed models in a true few-shot setting with the same sample size of each category, i.e., \(|K|=16\) in Table 3 and the confusion matrix of our model on each corpus are shown in Appendix A. Generally, we can observe that all prompt-based models have a smaller margin of error than fine-tune, indicating that adding prompts tends to achieve stable performance.  
In addition, our proposal is the best performer among all discussed models, with a noticeable accuracy improvement. For instance, our model presents an improvement of 1.57%, 0.57%, 0.73%, 2.64%, and 0.5% in terms of accuracy against the best performing baseline on AFQMC, OCNLI, TNEWS, INEWS, and BQ, respectively. These overwhelming results indicate that our proposal leads to consistent gains in a majority cross Chinese text classification tasks. Moreover, the major difference between our proposal and LM-BFF is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**LM Params**} & \multicolumn{2}{c}{**Prompt Designing**} & \multirow{2}{*}{**Prompt Style**} & \multirow{2}{*}{**Fine-Tune Strategy**} \\ \cline{3-3} \cline{5-6}  & & **Templates** & & & \\ \hline Fine-tune & Tuned & — & — & — & — \\ PET & Tuned & Hand-craft & Hand-craft & Discrete & Model Ensemble \\ LM-BFF & Tuned & Auto & Auto & Discrete & Demonstration \\ P-tuning & Frozen & Auto & Auto & Gradient & Tuning-free \\ \hline Ours & Tuned & Auto & Auto & Discrete & Demonstration \\ \hline \hline \end{tabular}
\end{table}
Table 2: An organization of baselines.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AFQMC** & **OCNLI** & **TNEWS** & **INEWS** & **BQ** \\ \hline Fine-tune & 54.57 (\(\pm\)3.8) & 52.36 (\(\pm\)6.5) & 46.92 (\(\pm\)2.1) & 44.45 (\(\pm\)2.0) & 47.52 (\(\pm\)4.3) \\ PET & 62.43 (\(\pm\)3.2) & 58.16 (\(\pm\)4.7) & 51.2 (\(\pm\)1.7) & 49.92 (\(\pm\)1.5) & 53.61 (\(\pm\)3.9) \\ LM-BFF & 75.64 (\(\pm\)2.2) & 73.82 (\(\pm\)3.7) & 70.44 (\(\pm\)0.9) & 68.71 (\(\pm\)0.8) & 76.38 (\(\pm\)2.4) \\ P-tuning & 73.59 (\(\pm\)2.3) & 70.46 (\(\pm\)3.4) & 69.12 (\(\pm\)0.7) & 67.32 (\(\pm\)0.6) & 68.52 (\(\pm\)1.7) \\ \hline Ours & **77.21 (\(\pm\)2.3)** & **74.39 (\(\pm\)3.5)** & **71.17 (\(\pm\)1.0)** & **71.35 (\(\pm\)0.4)** & **76.88 (\(\pm\)2.0)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance. The result of the best performing baseline and the best performer in each column is underlined and boldfaced, respectively.  
the demonstration strategy. Therefore, the comparison of our proposal against LM-BFF illustrates the strength of our proposed joint correlation scoring function.  
Further, we find that P-tuning under-performs LM-BFF on all discussed tasks. For example, LM-BFF achieves an accuracy improvement of 2.05%, 3.36%, and 1.32% on AFQMC, OCNLI, and TNEWS against P-tuning. It can be explained by the fact that the combination of discrete prompts and demonstration learning prefers fewer inputs than continuous prompts. Regarding the template style, automatically generated templates generally outperform the hand-crafted templates on all datasets. For example, PET shows an accuracy decrease of 22.77%, 14.91%, and 23.27% on BQ against LM-BFF, P-tuning, and our proposal, which reflects that although a manual prompt is more intuitive than an automated one, it is more easily trapped in the local optimist. Moreover, automatically generated templates perform more stably than manually designed ones, meaning that automatically generated templates have more generalization capabilities.