## 5 Experimental Results and Analyses
### Ablation Study  
This section analyzes the various modules of the proposed research method to evaluate their contributions to the overall architecture. * The effect of the complexity classifier In Tables 2 and 3, the single-step retrieval and multi-step retrieval methods do not assess the complexity of the queries, applying a single retrieval generation strategy for all cases. Our proposed method demonstrates significant improvements in both performance and efficiency, validating the effectiveness of the adaptive retrieval strategy. * The effect of the first-stage retriever We varied the top-k values while keeping other parameters constant. As shown in Table 4, increasing the top-k values results in a decrease in efficiency, accuracy, and F1 score. However, this impact is relatively small. It is hypothesized that as top-k increases, the volume of retrieved information rises, leading to the accumulation of irrelevant and redundant data, which can negatively affect the LLM's responses. Furthermore, when the LLM receives longer texts, the processing time increases, requiring more time to generate responses. * The effect of different LLMs We conducted experiments using the GPT-4o model on three multi-hop datasets. Table 5 demonstrates that, compared to the Llama3-8B model, the GPT-4o model improves the F1 score and accuracy by an average of over 10% across the multi-hop datasets. The use of a more powerful model in our proposed method leads to better performance. This also indirectly suggests that our strategy effectively retrieves relevant information, as there may be instances where the LLM alone does not identify relevant clues. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Top-k** & **F1** & **Acc** & **Step** & **Time** \\ \hline
3 & **51.64** & **34.80** & **3.43** & **4.30** \\
4 & 47.06 & 31.05 & 4.78 & 5.78 \\
5 & 46.46 & 30.26 & 6.78 & 8.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the Llama3-8B on HotpotQA under different Top-k conditions with the first-stage retriever. \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}{**SQuAD**} & \multicolumn{4}{c}{**Natural Questions**} & \multicolumn{4}{c}{**TriviaQA**} \\ \cline{3-13}  & & **F1** & **Acc** & **Step** & **Time** & **F1** & **Acc** & **Step** & **Time** & **F1** & **Acc** & **Step** & **Time** \\ \hline \multirow{3}{*}{Simple} & Non-Retrieval & 10.50 & 5.00 & 0.00 & 0.11 & 19.00 & 15.60 & 0.00 & 0.13 & 31.80 & 27.00 & 0.00 & 0.13 \\  & Single-Step Retrieval & 39.30 & 34.00 & 1.00 & 1.00 & 47.30 & 44.60 & 1.00 & 1.00 & 62.40 & 60.20 & 1.00 & 1.00 \\ \multirow{3}{*}{Complex} & Multi-Step Retrieval & 35.60 & 29.60 & 4.52 & 9.03 & 47.80 & 44.20 & 5.04 & 10.18 & 62.40 & 60.20 & 5.28 & 9.22 \\  & Adaptive Retrieval & 23.10 & 17.60 & 0.50 & 0.55 & 36.00 & 33.00 & 0.50 & 0.56 & 46.90 & 42.60 & 0.50 & 0.56 \\  & Self-RAG & 11.20 & 18.40 & 0.63 & 0.50 & 39.00 & 33.60 & 0.63 & 0.17 & 29.30 & 57.00 & 6.88 & 0.45 \\  & Adaptive-RAG & 38.30 & 33.00 & 1.37 & 2.02 & 47.30 & 44.60 & 1.00 & 1.00 & 60.70 & 58.20 & 1.23 & 1.54 \\  & Our Method & **46.50** & **43.40** & **3.73** & **4.30** & **60.16** & **58.8** & **3.24** & **3.41** & **70.20** & **68.80** & **3.84** & **4.50** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results obtained from the Llama3-8B model on the single-hop datasets are compared with those of other methods. Our results are highlighted in bold for emphasis. * The effect of the relevance classifier  
Table 4 shows that as the top-k value increases, more information is retrieved, leading to an increase in noisy data. This raises the question: can the relevance classifier help reduce irrelevant information? Table 6 indicates that when the relevance classifier is removed, the F1 score and accuracy decrease by an average of 2.38% and 1.67%, respectively. This highlights the necessity of incorporating the relevance classifier.