## 5 Experimental Results and Analyses
### Main Results  
We conducted experiments on three multi-hop datasets and compared the results with those of other methods. We classified previous studies into three groups: simple, complex, and adaptive. Table 2 shows that the accuracy of non-retrieval-augmented methods is low, while multi-hop retrieval methods achieve high accuracy but exhibit low efficiency, requiring considerable processing time. Adaptive retrieval methods, by distinguishing between simple and complex queries, apply different strategies and achieve relatively high accuracy.  
Our proposed method demonstrates both high accuracy and efficiency, particularly on the HotpotQA dataset, where it surpasses the Adaptive-RAG method, improving accuracy by 9.4% and the F1 score by 16.14%. The step metric reveals that our method involves multiple retrievals. For complex queries, retrieving information just once may not provide sufficient data; hence, our approach conducts multiple queries to address this gap. In contrast, the non-retrieval, single-step retrieval, and multi-step retrieval methods apply a single strategy for any given query. Our adaptive retrieval method, however, enhances model accuracy while only marginally increasing the time spent on retrieval and generation.  
We conducted experiments using our proposed research method on three single-hop datasets. Table 3 shows that our method outperforms others by more than 10% in both accuracy and F1 score. Upon analyzing the experimental results, we observed that some single-hop queries are relatively complex and can be decomposed into two sub-queries, allowing for multiple retrievals to gather more relevant information.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**MuSiQue**} & \multicolumn{4}{c}{**HotpotQA**} & \multicolumn{4}{c}{**2 Wiki**} \\ \cline{3-14}  & & **F1** & **Acc** & **Step** & **Time** & **F1** & **Acc** & **Step** & **Time** & **F1** & **Acc** & **Step** & **Time** \\ \hline \multirow{3}{*}{Simple} & Non-Retrieval & 10.70 & 3.20 & 0.00 & 0.11 & 22.71 & 17.20 & 0.00 & 0.11 & 32.04 & 27.80 & 0.00 & 0.10 \\  & Single-Step Retrieval & 22.80 & 15.20 & 1.00 & 1.00 & 46.15 & 36.40 & 1.00 & 1.00 & 47.90 & 42.80 & 1.00 & 1.00 \\ \multirow{3}{*}{Complex} & Multi-Step Retrieval & 31.90 & 25.80 & 3.60 & 7.58 & 56.54 & 47.00 & 5.53 & 9.38 & 58.85 & 55.40 & 4.17 & 7.37 \\  & Adaptive Retrieval & 15.80 & 8.00 & 0.50 & 0.55 & 32.22 & 25.00 & 0.50 & 0.55 & 39.44 & 34.20 & 0.50 & 0.55 \\ \multirow{3}{*}{Adaptive} & Self-RAG & 8.10 & 12.00 & 0.73 & 0.51 & 17.53 & 29.60 & 0.73 & 0.45 & 19.59 & 38.80 & 0.93 & 0.49 \\  & Adaptive-RAG & 31.80 & 26.00 & 3.22 & 6.61 & 53.82 & 44.40 & 3.55 & 5.99 & 49.75 & 46.40 & 2.63 & 4.68 \\ \multirow{3}{*}{Complex} & Our Method & **41.97** & **26.55** & **3.88** & **4.45** & **69.96** & **53.8** & **3.42** & **3.92** & **54.65** & **37.60** & **3.88** & **4.32** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results obtained from our proposed method on the multi-hop datasets using the Llama3-8B model are compared with those of other methods. Our results are highlighted in bold for emphasis.  
For simple queries, apart from the multi-step retrieval method, all other methods classify them as simple and do not require multiple retrievals. Our proposed research method places greater emphasis on classifying the complexity of queries, demonstrating strong sensitivity to this factor.  
Through a comparative analysis of three public datasets, we observed that the LQR method demonstrates superior performance. The following section focuses on examining the contribution of each component within the LQR method.