# Evaluating Complex Entity Knowledge Propagation for Knowledge Editing in LLMs
## 7 Conclusions  
With each passing day, new facts are emerging and, in many cases, older facts are being updated. Language models are playing a crucial role in technological advancement in multiple fields of applications and are making life easier in day-to-day life. Editing language models continuously is a necessity to keep the models up-to-date. However, updating models faces multiple challenges and edits are not always in depth. Updating a language model requires editing all the directly and indirectly linked relevant facts, making sure unrelated or irrelevant facts remain unchanged. For KE, in this study, we proposed an extensive triplet representation for complex entity knowledge propagation. We created the CTR-KE dataset with references from Wikipedia and Wikidata with fifty entities and five hops to evaluate how much the editing methods can actually edit when the depth or content related to a specific entity increases or exists in an ample amount. We prepared the dataset with the main or head entity containing ten \((h,r,t)\) combinations in the first hop and every tail entity considered as the head entity for the next hop. From the second hop to the fifth hop, we considered three combinations of \((h,r,t)\) for every head entity. We defined and compared how the parameters were updated with the complex triplet representation to edit the language models. We successfully tested and proved our hypothesis that deeper contextual representation through recursive triplets results in contextual enrichment of the editing methods and reduces hallucinations or catastrophic forgetting. We evaluated our proposed comprehensive triplet representation for complex entity knowledge propagation using five evaluation metrics, i.e., Gn, HEA, CI, Fo, and Sp, and four knowledge editing methods, i.e., Fine\(-\)tuning, ROME, MEMIT and IKE. We provided pre- and post-editing results to convey that multiple triplet updates are more convenient and accurate completed by MEMIT and IKE. We experimented with three language models, i.e., GPT2-XL, GPT-J, and Llama-2-7b. The proposed work shows promising results and paves the way for more research that concentrates on the depth of information storage in large language models, data representation, and the editing process for a deeper understanding of the contexts. Graph traversal for knowledge propagation, including more entities and data, are the aims of our future research. Conceptualization, W.S. and S.-H.N. ; Methodology, W.S. ; Software, W.S. ; Validation, W.S. and S.-H.N. ; Formal analysis, W.S. ; Investigation, W.S. and S.-H.N. ; Resources, S.-H.N. ; Data curation, W.S. ; Writing\(-\)original draft, W.S. ; Writing\(-\)review & editing, W.S. and S.-H.N. ; \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** & **Pr-Ed** & **Po-Ed** \\ \hline Gn & STR & 34.1 & 47.8 & 46.3 & 58.9 & 48.1 & 60.3 & 52.5 & 61.3 \\  & CTR & 37.8 & 50.3 & 48.0 & 59.2 & 48.9 & 63.9 & 55.3 & 62.9 \\  & STR & 53.2 & 68.3 & 54.6 & 69.2 & 63.6 & 78.1 & 76.8 & 81.4 \\  & CTR & 56.7 & 71.8 & 59.2 & 73.5 & 66.1 & 79.2 & 77.2 & 83.8 \\  & STR & 47.9 & 55.8 & 53.6 & 60.3 & 61.6 & 68.9 & 61.1 & 68.3 \\  & CTR & 51.2 & 56.2 & 55.6 & 62.5 & 63.7 & 73.1 & 67.2 & 73.4 \\  & STR & 48.2 & 58.7 & 58.9 & 67.8 & 63.6 & 76.2 & 65.2 & 77.9 \\  & CTR & 53.3 & 60.7 & 62.4 & 70.3 & 67.9 & 82.6 & 69.3 & 81.5 \\  & STR & 43.9 & 52.3 & 53.6 & 61.6 & 60.4 & 69.3 & 62.4 & 70.2 \\ Sp & CTR & 47.2 & 51.5 & 55.9 & 66.2 & 61.3 & 69.8 & 66.7 & 74.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy on the Llama-2-7b language model using the different editing methods with their semantic triplet representation (STR) and our comprehensive triplet representation (CTR). The results are given for the pre-edit (Pr-Ed) and post-edit (Po-Ed). Visualization, W.S. ; Supervision, S.-H.N. ; Project administration, S.-H.N.