## 6 Discussion  
The experimental results on both single-hop and multi-hop datasets demonstrate that the adaptive retrieval approach shows promising results in retrieval-augmented generation. Compared to other adaptive retrieval methods, e.g., self-RAG [32], adaptive-RAG [23], and parametric and non-parametric memory-based methods [31], our proposed LQR not only identifies query complexity but also focuses more on finding documents relevant to the query. Specifically for complex queries, we employ multi-step retrieval, relevance recognition, and other techniques to locate pertinent information while simultaneously filtering out irrelevant content. In the process of retrieval-augmented generation, it is crucial not only to focus on the performance of the generative model but also to retrieve documents that can effectively answer the query. Retrieval-augmented generation must balance the quality of the generated responses with the time consumed throughout the process. In multi-step methods, continuous retrieval and generation cycles eventually lead to the final answer. However, this approach is time-consuming and resource-intensive. On the other hand, single-step retrieval is faster, but the quality of the generated output is often suboptimal. The challenge becomes even more pronounced when dealing with complex queries--how can we ensure that all relevant documents are retrieved? To further evaluate the model, we conducted a case study using data related to sheep. For instance, when presented with the questions \"What is the scientific name of a domestic sheep?\" and \"Which breed of sheep is native to China and known for producing fine wool?\", the LLM demonstrated its ability to not only retrieve relevant information from the context but also leverage its pre-trained knowledge to provide more comprehensive responses. This highlights the role of the LLM's pre-trained world knowledge in enhancing answer quality. However, the case study also revealed challenges. Irrelevant information occasionally emerged during the retrieval process, which could affect the quality of the model's responses. Despite these issues, LQR demonstrated greater flexibility compared to single-step and multi-step retrieval methods. Nonetheless, further optimization of LQR's individual components remains necessary to fully realize its potential. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Query and Answer** & **Llama3-8B** & **GPT-4o** & **Complexity Classification** \\ \hline \multirow{4}{*}{MusiQue} & **Query:** What field does the author of & & & Complex \\  & Parallel Worlds work in? & Physics & Science & Q1: Who is the author of \\  & **Answer:** physics & & & Parallel Worlds? \\  & **Query:** [PERSON] trained under the & & & Q2: What field does the author of Parallel Worlds \\ \multirow{4}{*}{HotpotQA} & **Query:** [PERSON] trained under of which type of & & & \\  & dance analysis? & & Laban Movement Analysis & Simple \\  & **Answer:** Laban Movement Analysis & & & Complex \\  & **Query:** Which country the founder of & & & Q1: Who founded the Team \\ \multirow{2}{*}{2 Wiki} & **Query:** Team Red Bull (Nascar Team) is from? & Austrian & Austria & Red Bull NASCAR team? \\  & **Answer:** Austria & & & Q2: What country is the founder of the Team Red Bull \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Examples of responses from our proposed method using Llama3-8B and GPT-4o on three multi-hop datasets. This study reveals that for particularly complex queries, the proposed method struggles to retrieve effective documents. How to optimize retrieval and improve document relevance recognition will be key research directions in the future. Our experiments indicate that using larger models yields higher accuracy. There are two reasons for this. First, larger models possess stronger comprehension capabilities, allowing them to find clues in the documents that lead to the correct answer. Interestingly, we observed that certain queries received correct answers even without retrieving all relevant documents.