# Generative Aspect Sentiment Quad Prediction with Self-Inference Template
## 5 Results and Discussion
### Determination of Prefix Prompts  
Currently, prefix prompts can be broadly categorized into hard prompts and soft prompts [14]. Hard prompts, also known as discrete prompts, are manually crafted prompts typically consisting of semantically meaningful phrases. On the other hand, soft prompts, also known as continuous prompts, are continuously updated and iterated during training, resembling a kind of updatable parameter without clear human-interpretable semantics. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Rest15**} & \multicolumn{4}{c}{**Rest16**} & \multicolumn{4}{c}{**ACOS\_Restaurant**} & \multicolumn{4}{c}{**ACOS\_Laptop**} \\  & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** \\ \hline HGCN-BERT+BERT-Linear \({}^{*}\) & 24.43 & 20.25 & 22.15 & 25.36 & 24.03 & 24.68 & - & - & - & - & - \\ HGCN-BERT+BERT-TFM \({}^{*}\) & 25.55 & 22.01 & 23.65 & 27.40 & 26.41 & 26.90 & - & - & - & - & - \\ TASO-BERT-Linear \({}^{*}\) & 41.86 & 26.50 & 32.46 & 49.73 & 40.70 & 44.77 & - & - & - & - & - \\ TASO-BERT-CRF \({}^{*}\) & 44.24 & 28.66 & 34.78 & 48.65 & 39.68 & 43.71 & - & - & - & - & - \\ TAS-BERT-ACOS \({}^{\blacktriangle}\) & - & - & - & - & - & - & 26.29 & 46.29 & 33.53 & 47.15 & 19.22 & 27.31 \\ Extract-Classify-ACOS \({}^{\blacktriangle}\) & 35.64 & 37.25 & 36.42 & 38.40 & 50.93 & 43.77 & 38.54 & 52.96 & 44.61 & 45.56 & 29.48 & 35.80 \\ GAS \({}^{\blacktriangle}\) & 45.31 & 46.70 & 45.98 & 54.54 & 57.62 & 56.04 & 53.57 & 54.34 & 53.95 & 40.70 & 40.17 & 40.43 \\ Seq2 Path \({}^{\blacktriangle}\) & - & - & - & - & - & - & 62.38 & 55.02 & 58.47 & 41.46 & 41.00 & 41.23 \\ Paraphrase \({}^{\blacktriangledown}\) & 46.16 & 47.72 & 46.93 & 56.63 & 59.30 & 57.93 & 61.02 & 59.73 & 60.37 & 44.87 & 44.10 & 44.48 \\ DLO \({}^{*}\) & 47.07 & 49.33 & 48.18 & 57.92 & 61.80 & 59.79 & - & - & - & - & - & - \\ ILO \({}^{*}\) & 47.78 & 50.38 & 49.05 & 57.58 & 61.17 & 59.32 & - & - & - & - & - & - \\ \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 \\ SIT+PT & 48.41 & 49.75 & 49.07 & **60.78** & **63.24** & **61.99** & **63.54** & **63.83** & **63.69** & 43.12 & 42.78 & 42.95 \\ SIT+MT & 47.93 & 49.50 & 48.70 & 58.30 & 60.96 & 59.60 & 61.79 & 63.27 & 62.52 & 44.46 & 44.35 & 44.41 \\ SIT+PM & **49.63** & **50.38** & **50.00** & 59.22 & 61.66 & 60.44 & 62.88 & 63.38 & 63.13 & **45.95** & **45.91** & **45.93** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results compared with baseline methods in terms of precision (Pre, %), recall (Rec, %) and F1 score (F1, %). PT stands for the Add Prompt method, MT stands for the Mask Tokens method, and PM represents the combination of both the Add Prompt and Mask Tokens methods. The best scores are marked in bold. The prefix prompts are the optimal prompts for each dataset in Section 5.2. For Rest15 and Rest16, the experimental results of the baseline methods, \(*\) are from [10], and \(\star\) are from [11]. For ACOS_Restaurant and ACOS_Laptop, the experimental results of the baseline methods, \(\blacktriangle\) are from [9], \(\blacktriangle\) are from [25], and \(\blacktriangle\) are from [24]. \(\blacktriangledown\) indicates the reproduction of the official method on our dataset. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Running Time**} \\  & **Rest15** & **Rest16** & **ACOS\_Restaurant** & **ACOS\_Laptop** \\ \hline Paraphrase & 152.24 & 224.81 & 266.91 & 501.65 \\ SIT & 151.16 & 225.55 & 263.80 & 495.60 \\ SIT+PT & 153.52 & 224.05 & 259.83 & 495.52 \\ SIT+MT & 154.39 & 225.97 & 268.03 & 496.58 \\ SIT+PM & 153.86 & 225.32 & 270.12 & 498.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model Runtime (Unit: Seconds).