# Multi-Task Supervised Alignment Pre-Training for Few-Shot Multimodal Sentiment Analysis
## 4 Experiments
### Ablation Experiment
#### 4.4.2 Ablation of Prompt Templates  
To assess the impact of prompt template variations on model performance, as shown in Table 6, we conducted ablation experiments across coarse-grained, fine-grained, and MSA tasks. These experiments focused on modifying the prompt templates of downstream tasks, generating multiple variants to evaluate their effects. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Prompt** & **Acc** & **Weighted-F1** \\ \hline \multirow{4}{*}{MVSA-S} & P1: Image: HV: Text: Sentiment of the text: [MASK], & 64.87 (\(\pm\)0.52) & 63.57 (\(\pm\)0.55) \\  & P2: Given the image HV and text T, the sentiment is [MASK], & 64.83 (\(\pm\)0.48) & 63.79 (\(\pm\)0.51) \\  & P3: For the image HV and the description T, the predicted sentiment is [MASK], & 64.81 (\(\pm\)0.51) & 63.80 (\(\pm\)0.48) \\  & P4: Analyze the image HV and text T. The emotional tone is [MASK], & 64.79 (\(\pm\)0.48) & 63.83 (\(\pm\)0.52) \\ \hline \multirow{4}{*}{MVSA-M} & P1: Image: HV: Text: Sentiment of the text: [MASK], & 62.75 (\(\pm\)0.28) & 59.56 (\(\pm\)1.20) \\  & P2: Given the image HV and text T, the sentiment is [MASK], & 62.69 (\(\pm\)0.25) & 59.36 (\(\pm\)1.10) \\  & P3: For the image HV and the description T, the predicted sentiment is [MASK], & 62.71 (\(\pm\)0.23) & 59.32 (\(\pm\)1.12) \\  & P4: Analyze the image HV and text T. The emotional tone is [MASK], & 62.69 (\(\pm\)0.21) & 59.33 (\(\pm\)1.15) \\ \hline \multirow{4}{*}{TumEmo} & P1: Image: HV: Text: Sentiment of the text: [MASK], & 43.48 (\(\pm\)0.22) & 49.43 (\(\pm\)0.31) \\  & P2: Given the image HV and text T, the sentiment is [MASK] & 43.45 (\(\pm\)0.25) & 49.39 (\(\pm\)0.29) \\  & P4: Analyze the image HV and the description T, the predicted sentiment is [MASK], & 43.44 (\(\pm\)0.24) & 49.40 (\(\pm\)0.27) \\  & P4: Analyze the image HV and the text T. The emotional tone is [MASK], & 43.41 (\(\pm\)0.25) & 49.41 (\(\pm\)0.26) \\ \hline \multirow{4}{*}{Twitree-15} & P1: Text: \"S\": Aspect: “A\". Sentiment of aspect: [MASK], & 63.48 (\(\pm\)0.22) & 62.66 (\(\pm\)0.47) \\  & P2: Given the text \"S\" and the aspect “A\". The associated sentiment is [MASK], & 63.45 (\(\pm\)0.23) & 62.67 (\(\pm\)0.45) \\ \cline{1-1}  & P3: In the text \"S\", the sentiment related to the aspect “A\" is [MASK], & 63.42 (\(\pm\)0.21) & 62.60 (\(\pm\)0.41) \\ \cline{1-1}  & P4: Analyze the text “S\". For the aspect “A\", the detected sentiment is [MASK], & 63.43 (\(\pm\)0.24) & 62.63 (\(\pm\)0.44) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparisons of different prompt templates on downstream tasks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Strategy** & **Acc** & **Weighted-F1** \\ \hline \multirow{4}{*}{MVSA-S} & \(f_{V}-f_{A}+\) & **64.87 (\(\pm\)0.50)** & **63.97 (\(\pm\)0.59)** \\  & \(f_{V}-f_{A}-\) & 52.21 (\(\pm\)6.92) & 54.55 (\(\pm\)6.06) \\  & \(f_{V}+f_{A}+\) & 52.37 (\(\pm\)0.31) & 54.79 (\(\pm\)0.09) \\  & \(f_{V}+f_{A}-\) & 49.43 (\(\pm\)0.64) & 51.98 (\(\pm\)1.33) \\ \hline \multirow{4}{*}{MVSA-M} & \(f_{V}-f_{A}+\) & **62.75 (\(\pm\)0.38)** & **59.36 (\(\pm\)1.50)** \\  & \(f_{V}-f_{A}-\) & 56.77 (\(\pm\)4.05) & 57.03 (\(\pm\)1.05) \\  & \(f_{V}+f_{A}+\) & 60.64 (\(\pm\)2.36) & 59.98 (\(\pm\)2.04) \\  & \(f_{V}+f_{A}-\) & 60.54 (\(\pm\)5.31) & 58.84 (\(\pm\)0.61) \\ \hline \multirow{4}{*}{TumEmo} & \(f_{V}-f_{A}+\) & **49.48 (\(\pm\)0.19)** & **49.47 (\(\pm\)0.29)** \\  & \(f_{V}-f_{A}-\) & 46.06 (\(\pm\)0.51) & 45.75 (\(\pm\)0.59) \\ \cline{1-1}  & \(f_{V}+f_{A}+\) & 44.50 (\(\pm\)0.88) & 44.60 (\(\pm\)1.05) \\ \cline{1-1}  & \(f_{V}+f_{A}-\) & 43.92 (\(\pm\)0.80) & 43.17 (\(\pm\)0.55) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparisons of different training strategies for downstream tasks. \(f_{V}\) is the vision encoder, \(f_{A}\) is the alignment layer, “+” denotes the trainable module, and “\(-\)” denotes the frozen module. Our findings indicate that while prompt design in few-shot learning influences performance, its overall impact remains limited.