# Evaluating Complex Entity Knowledge Propagation for Knowledge Editing in LLMs
## 6 Experiments and Results
The same goes for GPT-J and Llama-2-7b, as shown in Tables 5 and 6, where IKE and MEMIT performed better, with MEMIT demonstrating the most improved accuracy (\(\sim\)12 improved rate) compared to the pre-edit score. For \"HEA\" in GPT-J and Llama-2-7b, MEMIT had the best performance in terms of accuracy enhancement (i.e., when compared to the pre-edit accuracy), but IKE performed better with a score of 92.7 for GPT2-xl, 88.4 \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Fine-Tuning**} & \multicolumn{3}{c}{**ROME**} & \multicolumn{3}{c}{**MEMIT**} & \multicolumn{3}{c}{**IKE**} \\  & & **Pr-Ed** & **Po-Ed** & **Pr-Ed** & **Po-Ed** & **Pr-Ed** & **Po-Ed** & **Pr-Ed** & **Po-Ed** \\ \hline \multirow{3}{*}{Gn} & STR & 39.5 & 46.4 & 35.2 & 44.7 & 47.9 & 52.9 & 49.0 & 55.1 \\  & CTR & 41.3 & 50.9 & 40.5 & 49.5 & 45.2 & 52.3 & 51.2 & 59.3 \\  & STR & 73.9 & 89.3 & 71.2 & 86.1 & 81.5 & 90.1 & 86.4 & 91.7 \\  & CTR & 76.8 & 89.9 & 74.1 & 88.4 & 84.0 & 92.3 & 87.0 & 92.7 \\  & STR & 38.2 & 46.7 & 35.9 & 45.4 & 39.1 & 47.2 & 47.2 & 50.2 \\  & CTR & 39.0 & 46.1 & 40.1 & 46.9 & 43.2 & 50.3 & 48.9 & 51.6 \\  & STR & 60.2 & 67.8 & 59.0 & 67.2 & 61.4 & 68.4 & 62.1 & 69.5 \\  & CTR & 64.5 & 71.2 & 62.4 & 70.1 & 65.2 & 72.5 & 66.4 & 74.8 \\  & STR & 37.1 & 40.3 & 55.2 & 65.7 & 59.8 & 69.2 & 63.6 & 73.4 \\  & CTR & 39.8 & 45.6 & 57.9 & 69.3 & 61.4 & 70.7 & 66.8 & 74.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy on the GPT2-XL language model using different editing methods with their semantic triplet representation (STR) and our comprehensive triplet representation (CTR). The results are given for pre-edit (Pr-Ed) and post-edit (Po-Ed). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Fine-Tuning**} & \multicolumn{3}{c}{**ROME**} & \multicolumn{3}{c}{**MEMIT**} & \multicolumn{3}{c}{**IKE**} \\  & & **Pr-Ed** & **Po-Ed** & **Pr-Ed** & **Po-Ed** & **Pr-Ed** & **Po-Ed** & **Pr-Ed** & **Po-Ed** \\ \hline \multirow{3}{*}{Gn} & STR & 38.8 & 49.2 & 42.5 & 53.5 & 43.3 & 55.6 & 56.3 & 62.3 \\  & CTR & 39.3 & 49.8 & 46.7 & 55.8 & 48.9 & 59.3 & 55.7 & 62.1 \\  & STR & 69.8 & 78.4 & 73.2 & 79.0 & 75.1 & 85.6 & 76.2 & 87.1 \\  & CTR & 71.2 & 79.1 & 76.0 & 84.5 & 76.4 & 87.2 & 79.3 & 88.4 \\  & STR & 44.3 & 52.6 & 43.7 & 48.9 & 49.2 & 58.6 & 52.1 & 58.9 \\  & CTR & 45.4 & 53.1 & 44.8 & 50.2 & 51.1 & 59.7 & 51.5 & 58.7 \\  & STR & 54.8 & 65.7 & 59.9 & 70.2 & 63.9 & 75.4 & 73.2 & 80.1 \\  & CTR & 55.2 & 66.9 & 63.2 & 74.5 & 66.8 & 78.5 & 73.9 & 81.2 \\  & STR & 37.2 & 46.1 & 56.2 & 67.4 & 67.2 & 75.3 & 70.1 & 79.0 \\  & CTR & 36.6 & 45.2 & 58.8 & 69.0 & 68.9 & 77.8 & 71.2 & 79.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy on the GPT-J language model using different editing methods with their semantic triplet representation (STR) and our comprehensive triplet representation (CTR). The results are given for pre-edit (Pr-Ed) and post-edit (Po-Ed). for GPT-J, and 83.8 for Llama-2-7b. For \"CI\" in GPT2-XL, IKE had the best score of 51.6, whereas MEMIT performed better than IKE with an accuracy score of 59.7 for GPT-J. In Llama-2-7b, MEMIT and IKE almost had the same performance. For \"Fo\" and \"Sp\", IKE had the best accuracy score in all three LLMs. The proposed complex representation of the triplet enhances the performance of the editing methods and not only improves the generalizability and specificity but also produces better results for compositionality and forgetfulness.