# Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
## 5 Discussion  
### Ablation Study  
We conducted ablation experiments using DeepSeek-v3 as the baseline model on both the SemEval-2016 Task 6 and P-Stance datasets to investigate the impact of each module in the ZSMD framework, while evaluating the performance of ZSMD when individual modules are removed. The results are presented in Table 3.  
The variants of ZSMD are as follows:  
* \"w/o KA\": The knowledge augmentation module is removed, meaning no external knowledge is supplemented for the tweets or targets. The debater agents rely solely on the original tweet content to formulate arguments and engage in debate. This configuration assesses the impact of background knowledge on stance detection performance. * \"w/o DM\": The debate mechanism is eliminated, and only the initially generated arguments are used as the basis for the final stance determination, without undergoing multiple rounds of argumentation or viewpoint updates. This setup evaluates the role of the debate mechanism in refining viewpoints and enhancing reasoning capabilities. * \"w/o Debate\": The entire debate process is completely removed, and the stance determination is made directly by the referee agent based solely on the initial arguments of the agents, without any iterative debate. This tests the contribution of the full debate process to the ultimate stance classification task. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & \multicolumn{6}{c}{**Sem16 (\%)**} & \multicolumn{6}{c}{**P-Stance (\%)**} \\ \cline{2-10}  & **HC** & **FM** & **LA** & **A** & **CC** & **Avg** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Avg** \\ \hline Baseline Models & & & & & & & & & \\ JointCL & 54.80 & 53.80 & 49.50 & 54.50 & 39.70 & 50.46 & - & - & - & - \\ KASD-LLaMA2 & 77.70 & 65.57 & 57.07 & 39.55 & 50.72 & 50.98 & 75.28 & 74.09 & 69.27 & 72.88 \\ KASD-ChatGPT & 80.32 & 70.41 & 62.71 & 63.95 & 55.83 & 66.64 & 83.60 & 79.66 & 84.31 & 82.52 \\ KAI & 76.40 & 73.70 & 69.40 & - & - & - & 85.70 & 80.50 & 75.90 & 80.70 \\ MBCal-LLaMA2 & 77.19 & 74.71 & 72.49 & 58.29 & 67.71 & 70.08 & 84.04 & 81.22 & 77.57 & 80.94 \\ MBCal-ChatGPT & 80.26 & 75.76 & 68.77 & 66.54 & **71.00** & 72.47 & 85.14 & 81.05 & 85.08 & 83.76 \\ KEL-CA & 81.70 & 72.30 & **73.30** & 68.30 & 65.70 & 72.26 & - & - & - & - \\ COLA & 81.70 & 63.40 & 71.00 & 70.80 & 67.50 & 70.88 & 84.00 & 79.70 & 86.60 & 83.43 \\ \hline Our Models & & & & & & & & & \\ GPT3.5+ZSMD & 82.20 & 73.82 & 72.71 & 70.94 & 68.43 & 73.62 & 85.91 & 81.10 & 85.26 & 84.09 \\ DeepSeek-v3+ZSMD & **86.32** & **76.43** & 73.08 & **72.88** & 70.42 & **75.83** & **87.67** & **83.71** & **88.19** & **86.52** \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of ZSMD and baselines in zero-shot stance detection task. Bold and underlined values refer to the best and second-best performance. For clarity, the target abbreviations used in the table are [PERSON] (HC), feminist movement (FM), legalization of abortion (LA), atheism (A), and climate change is a real concern (CC). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline  & \multicolumn{6}{c}{**Sem16 (\%)**} & \multicolumn{6}{c}{**P-Stance (\%)**} \\ \cline{2-9}  & **HC** & **FM** & **LA** & **A** & **CC** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline DeepSeek-v3+ZSMD & 86.32 & 76.43 & 73.08 & 72.88 & 70.42 & 87.67 & 83.71 & 88.19 \\ w/o KA & 83.16 & 74.85 & 72.10 & 71.47 & 68.55 & 85.36 & 82.02 & 86.40 \\ w/o DM & 82.47 & 72.63 & 70.40 & 70.28 & 67.91 & 82.06 & 76.90 & 86.15 \\ w/o Debate & 80.76 & 71.08 & 68.92 & 67.17 & 65.36 & 80.88 & 71.62 & 69.36 \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental results of ablation study. In Figure 3, we systematically evaluate the impact of the number of debate rounds on the experimental results using DeepSeek-v3. The results show that when the number of debate rounds does not exceed two, model performance improves significantly with additional rounds, suggesting that agents effectively extract and leverage information from the tweet and background knowledge during the initial interactions.