# Knowledge Distillation-Based Multilingual Code Retrieval
## 4 Experiment Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**SuccessRate@k**} & **CODE** & **QUERY** & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Go**} & \multirow{2}{*}{**Java**} & \multirow{2}{*}{**Javascript**} & \multirow{2}{*}{**Php**} & \multirow{2}{*}{**Python**} & \multirow{2}{*}{**Ruby**} \\  & **ENCODE** & & & & & & & \\ \hline \multirow{9}{*}{SELF-ATT} & \multirow{9}{*}{SELF-ATT} & Go & **0.8581** & 0.6970 & 0.6100 & 0.6301 & 0.7476 & 0.664 \\  & & Java & 0.7936 & **0.7998** & 0.6325 & 0.6995 & 0.7826 & 0.6945 \\  & & Javascript & 0.7229 & 0.6749 & 0.6967 & 0.6129 & 0.7563 & 0.6755 \\  & & Php & 0.7831 & 0.7405 & 0.6550 & **0.7759** & 0.8486 & 0.7355 \\  & & Python & 0.7809 & 0.7215 & 0.6475 & 0.7154 & **0.9030** & 0.751 \\  & & Ruby & 0.6611 & 0.5995 & 0.5125 & 0.5355 & 0.7058 & 0.6785 \\  & & ALL & 0.8399 & 0.7777 & 0.6832 & 0.7636 & 0.8755 & 0.7455 \\  & & MPLCS & 0.8494 & 0.7910 & **0.7287** & 0.7703 & 0.8969 & **0.7930** \\ \hline \multirow{9}{*}{CNN} & \multirow{9}{*}{CNN} & Go & **0.8622** & 0.7146 & 0.6318 & 0.6672 & 0.7891 & 0.6945 \\  & & Java & 0.8104 & **0.8131** & 0.6694 & 0.7236 & 0.8215 & 0.7260 \\  & & Javascript & 0.7626 & 0.7114 & 0.7203 & 0.6492 & 0.7987 & 0.7020 \\  & & Php & 0.8054 & 0.7581 & 0.6771 & **0.7890** & 0.8722 & 0.7620 \\  & & Python & 0.8101 & 0.7507 & 0.6708 & 0.7234 & **0.9155** & 0.7710 \\  & & Ruby & 0.7373 & 0.6406 & 0.5624 & 0.5895 & 0.7678 & 0.7240 \\  & & ALL & 0.8447 & 0.7898 & 0.7008 & 0.7741 & 0.8906 & 0.7640 \\  & & MPLCS & 0.8523 & 0.8067 & **0.7410** & 0.7803 & 0.9117 & **0.8040** \\ \hline \multirow{9}{*}{NBOW} & \multirow{9}{*}{NBOW} & Go & **0.8208** & 0.6841 & 0.6033 & 0.5832 & 0.7196 & 0.6280 \\  & & Java & 0.7253 & 0.7624 & 0.6077 & 0.6280 & 0.7320 & 0.6595 \\  & & Javascript & 0.7025 & 0.6546 & 0.6593 & 0.5884 & 0.6902 & 0.5965 \\  & & Php & 0.7349 & 0.7003 & 0.6165 & **0.7291** & 0.7622 & 0.6660 \\  & & Python & 0.7449 & 0.6900 & 0.6068 & 0.6321 & **0.8422** & 0.6920 \\  & & Ruby & 0.6448 & 0.5913 & 0.5105 & 0.5153 & 0.6894 & 0.6565 \\  & & ALL & 0.8002 & 0.7361 & 0.6453 & 0.7034 & 0.8030 & 0.7045 \\  & & MPLCS & **0.8217** & **0.7626** & **0.6975** & 0.7156 & 0.8397 & **0.7455** \\ \hline \multirow{9}{*}{SELF-ATT} & \multirow{9}{*}{NBOW} & Go & **0.8508** & 0.6919 & 0.6198 & 0.6176 & 0.7395 & 0.6670 \\  & & Java & 0.7896 & **0.7934** & 0.6372 & 0.7000 & 0.7831 & 0.7010 \\ \cline{1-1}  & & Javascript & 0.7328 & 0.6732 & 0.7017 & 0.6421 & 0.7465 & 0.6570 \\ \cline{1-1}  & & Php & 0.7789 & 0.7322 & 0.6457 & **0.7755** & 0.8319 & 0.7205 \\ \cline{1-1}  & & Python & 0.7779 & 0.7183 & 0.6488 & 0.7069 & 0.8994 & 0.7480 \\ \cline{1-1}  & & Ruby & 0.6594 & 0.5858 & 0.5063 & 0.5126 & 0.6946 & 0.6775 \\ \cline{1-1}  & & ALL & 0.8367 & 0.7745 & 0.6892 & 0.7637 & 0.8750 & 0.7465 \\ \cline{1-1}  & & MPLCS & 0.8482 & 0.7918 & **0.7312** & 0.7715 & **0.9011** & **0.7865** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._which leads to the gradual increase in MMR across all models. When \(\lambda=0.8\), The average MRR for the six languages reached a maximum, relatively close to the MMR when \(\lambda=0.9\) and \(\lambda=1.0\). The results indicate that as more teacher models are instructed, the more knowledge student models are learned.