# Generative Aspect Sentiment Quad Prediction with Self-Inference Template
## 5 Results and Discussion
### Determination of Prefix Prompts
Training with soft prompts requires a substantial amount of data for iterative updates, and the existing datasets for aspect sentiment quadruplet prediction are relatively small, making them unsuitable for training with soft prompts. Therefore, we opt for hard prompts, where we manually create prompt texts to assist the model's understanding during training. We generated six prompt texts, as illustrated in Figure 6. Three of them were created based on the original template, and the other three were created based on the Self-Inference Template, informing the model about the task it needs to perform in three different forms. The experimental results are presented in Table 4.  
Based on the experimental results, it can be observed that, for the Rest15, Rest16, and ACOS_Restaurant datasets, the first type of prefix prompt, which directly instructs the model to rewrite, can significantly help improve the model's reasoning ability. For ACOS_Laptop, the third type of prefix, instructing the model to first identify the four sentiment elements and then rewrite, combined with the Mask operation, leads to the optimal results. The reason might be that Rest15, Rest16, and ACOS_Restaurant datasets have fewer aspect categories, allowing the model to adequately learn the data for each aspect category during training and understand the task requirements without the need for prompting the model to recognize sentiment elements. However, ACOS_Laptop has more aspect categories, and many of them have fewer occurrences, making it challenging for the \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Prompt Text**} & \multicolumn{3}{c}{**Rest15**} & \multicolumn{3}{c}{**Rest16**} & \multicolumn{3}{c}{**ACOS\_Restaurant**} & \multicolumn{3}{c}{**ACOS\_Laptop**} \\  & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** \\ \hline SIT & 47.89 & 50.13 & 48.98 & 58.98 & 61.60 & 60.26 & 63.13 & 63.49 & 63.31 & 44.38 & 44.61 & 44.49 \\ \hline +Prompt1 & 48.41 & 49.75 & 49.07 & **60.78** & **63.24** & **61.99** & **63.54** & **63.83** & **63.69** & 43.79 & 43.57 & 43.68 \\ +Prompt1+MT & **49.63** & **50.38** & **50.00** & 59.22 & 61.66 & 60.44 & 62.88 & 63.38 & 63.13 & 43.18 & 42.96 & 43.07 \\ +Prompt2 & 48.20 & 48.99 & 48.59 & 58.35 & 61.09 & 59.69 & 62.13 & 62.13 & 62.13 & 44.16 & 43.74 & 43.95 \\ +Prompt2+MT & 48.94 & 49.37 & 49.15 & 58.65 & 60.58 & 59.60 & 62.60 & 62.81 & 62.71 & 45.23 & 44.52 & 44.87 \\ \hline +Prompt3 & 46.99 & 48.11 & 47.54 & 53.50 & 56.15 & 54.79 & 61.43 & 61.22 & 61.33 & 44.20 & 43.74 & 43.97 \\ +Prompt3+MT & 45.41 & 46.10 & 45.75 & 57.89 & 60.46 & 59.14 & 61.20 & 62.59 & 61.88 & 44.70 & 44.70 & 44.70 \\ +Prompt4 & 43.85 & 43.07 & 43.46 & 54.25 & 58.30 & 56.20 & 58.68 & 58.62 & 58.65 & 41.99 & 41.48 & 41.73 \\ +Prompt4+MT & 46.45 & 46.98 & 46.71 & 54.43 & 56.02 & 55.22 & 58.33 & 58.73 & 58.53 & 42.45 & 41.83 & 42.14 \\ \hline +Prompt5 & 47.43 & 48.87 & 48.14 & 59.21 & 61.09 & 60.14 & 63.46 & 63.61 & 63.53 & 43.93 & 43.39 & 43.66 \\ +Prompt5+MT & 47.27 & 49.12 & 48.18 & 57.95 & 61.47 & 59.66 & 61.88 & 62.59 & 62.23 & 43.46 & 43.30 & 43.38 \\ +Prompt6 & 47.77 & 48.49 & 48.13 & 58.29 & 60.58 & 59.42 & 61.01 & 60.32 & 60.66 & 43.12 & 42.78 & 42.95 \\ +Prompt6+MT & 46.48 & 47.36 & 46.91 & 57.58 & 59.70 & 58.62 & 61.51 & 60.88 & 61.20 & **45.95** & **45.91** & **45.93** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental Results Combining Different Prefix Prompts with the Self-Inference Template. The best scores are marked in bold. Figure 6: Prefix Prompt Texts. model to fully learn each class of data, resulting in an insufficient understanding of the task requirements. Therefore, for ACOS_Laptop, the third type of prefix, prompting the model to recognize sentiment elements first and then rewrite, can provide the maximum assistance in helping the model quickly understand task requirements and enhance its capabilities.