# Cross-Domain Fake News Detection Using a Prompt-Based Approach
## 5 Results
### Prompt-Engineering Sensitivity  
Zero-shot learning is particularly sensitive to prompt design. Even minor variations in how a language model is instructed can significantly impact its performance [13; 22; 23]. This subsection investigates the susceptibility of the best-performing GPT2-D model to variations in prompt design, specifically focusing on the verbalizer and template formatting. * **Verbalizer wording:** We examine the impact of alternative verbalizer labels by comparing our default choices of \"fake/real\" with \"false/true\". Our findings demonstrate that the performance of the GPT2-D model is influenced by the specific wording used \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Train**} & \multicolumn{5}{c}{**Test**} \\  & & **Bus.** & **Edu.** & **Tech.** & **Entert** & **Politi.** & **Sports** & **Celeb.** \\ \hline \multirow{8}{*}{[16]} & Bus. & 77.42 & 73.33 & 70.00 & 63.33 & **86.67** & 70.00 & **54.00** \\  & Edu. & 50.00 & 95.53 & 93.33 & 53.33 & 96.67 & 53.33 & **53.50** \\  & Tech. & 46.67 & 93.33 & 97.32 & 46.67 & **100.00** & 50.00 & **50.50** \\  & Entert. & 46.67 & 43.33 & 46.67 & 80.43 & 50.00 & 46.67 & 50.50 \\  & Politi. & 50.00 & 96.67 & **100.00** & 50.00 & 97.94 & 50.00 & **53.00** \\  & Sports & 93.33 & **70.00** & **53.33** & 83.33 & **63.33** & 77.41 & **56.00** \\  & Celeb. & 36.67 & 50.00 & **63.33** & 26.67 & 60.00 & 50.00 & 77.83 \\ \hline \multirow{8}{*}{[21]} & Bus. & - & 66.67 & 76.67 & **66.61** & 70.00 & 56.67 & - \\  & Edu. & 56.67 & - & 56.67 & **60.00** & 53.33 & 46.67 & - \\  & Tech. & 50.00 & 86.67 & - & **63.33** & 96.67 & **76.67** & - \\  & Entert. & 53.33 & 50.00 & 50.00 & - & 50.00 & 53.33 & - \\  & Politi. & 50.00 & 96.67 & 93.33 & **56.67** & - & 53.33 & - \\  & Sports & 66.67 & 66.00 & 46.67 & 66.67 & 60.00 & - & - \\  & Celeb. & - & - & - & - & - & - & - \\ \hline \multirow{8}{*}{**GPT2**} & Bus. & 96.23 & 49.00 & 50.00 & 57.14 & 51.67 & 95.52 & 53.56 \\  & Edu. & 54.72 & 96.72 & 97.14 & 50.79 & 96.67 & 55.22 & 51.72 \\  & Tech. & 49.06 & 98.36 & 97.14 & 50.79 & 98.33 & 55.22 & 49.37 \\  & Entert. & 96.36 & 47.54 & 50.00 & **96.83** & 51.67 & 89.55 & 49.37 \\  & Politi. & 49.06 & 77.05 & 97.14 & 50.79 & **100.00** & **55.22** & 50.63 \\  & Sports & 79.25 & 49.18 & 49.30 & 96.83 & 51.67 & 97.01 & 48.55 \\  & Celeb. & 56.60 & 54.10 & 51.43 & 47.62 & 58.33 & 47.76 & 86.81 \\ \hline \multirow{8}{*}{**GPT2-D**} & Bus. & **100.00** & 50.82 & 50.00 & **93.65** & 55.00 & **97.01** & 53.92 \\  & Edu. & **64.15** & **100.00** & **97.18** & 53.97 & **98.33** & **61.19** & 52.66 \\  & Tech. & **52.83** & **100.00** & **100.00** & 53.97 & **100.00** & 55.22 & 49.60 \\ \cline{1-1}  & Entert. & **98.11** & **50.82** & **51.43** & **96.83** & **53.33** & **92.54** & **54.62** \\ \cline{1-1}  & Politi. & **58.49** & **96.72** & **100.00** & 50.79 & **100.00** & **55.22** & 52.77 \\ \cline{1-1}  & Sports & **98.11** & 50.82 & 50.00 & **100.00** & 53.33 & **100.00** & 51.45 \\ \cline{1-1}  & Celeb. & **62.26** & **62.30** & 57.14 & **58.73** & **68.33** & **64.18** & **87.86** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of model performance across different domains using the accuracy metric (A%). The bold values indicate the best scores. [Bus. : business; Edu. : education; Tech. : technology; Entert. : entertainment; Politi. : politics; Celeb. : celebrity.]