## 5 Experimental Results and Analyses
### Case Study  
We conducted a case study to observe the results of our proposed method across various datasets. We selected three cases from both single-hop and multi-hop datasets to examine whether the simple queries defined in the data require decomposition and to assess the output performance of the large model. Additionally, we evaluate the effectiveness of the retrieval strategy.  
Table 7 presents the inference results using Llama3-8B and GPT-4o on three single-hop datasets. The data in Table 7 indicate that some queries in the single-hop datasets also need to be broken down into multiple simpler query statements. The examples in Table A1 in Appendix A demonstrate that after splitting the user queries, more relevant information can be retrieved.  
Table 8 presents the inference results using Llama3-8B and GPT-4o on three multi-hop datasets. From Table 8, it can be observed that for complex queries, retrieval can be per  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MuSiQue**} & \multicolumn{2}{c}{**HotpotQA**} & \multicolumn{2}{c}{**2 Wiki**} \\ \cline{2-7}  & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** \\ \hline Ours & **41.97** & **26.55** & **69.96** & **53.80** & **54.65** & **37.60** \\ w/o rcls & 37.58 & 23.13 & 67.63 & 52.60 & 54.22 & 37.20 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of Llama3-8B on three datasets without the relevance classifier. “rcls” represents the relevance classifier.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Query and Answer**} & \multirow{2}{*}{**Llama3-8B**} & \multirow{2}{*}{**GPT-4o**} & \multirow{2}{*}{**Complexity Classification**} \\ \cline{3-4} \cline{6-6}  & & & & & Complex \\ \cline{2-6} \multirow{4}{*}{SQuAD} & **Query:** Who was the CEO of GE when it announced its & & & Q1: When did GE \\  & & & & announce its \\  & & & & & Ecoimagation program? \\  & & & & & Q2: Who was the CEO of \\ Natural Questions & & & & & GE at that time? \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table}
Table 7: Examples of responses from our proposed method using Llama3-8B and GPT-4o on three single-hop datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MuSiQue**} & \multicolumn{2}{c}{**HotpotQA**} & \multicolumn{2}{c}{**2 Wiki**} \\ \cline{2-7}  & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** \\ \hline Llama-8B & 41.97 & 26.55 & 69.96 & 53.80 & 54.65 & 37.60 \\ GPT4o & **59.94** & **42.80** & **79.37** & **68.60** & **59.55** & **42.40** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of Llama3-8B and GPT-4o on the MusiQue, HotpotQA, and 2 Wiki multi-hop datasets.  
formed directly without splitting, which helps reduce the impact of irrelevant information. By combining Tables 7, 8, A1, and A2, it is evident that GPT-4o is more adept at extracting key information from long contexts. The experiments revealed that contextual information can assist the LLM in leveraging stored world knowledge, thereby enhancing the accuracy of responses.