# A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering
## 2 Materials and Methods  
### HTGQE Framework and Task Definition  
The framework of HTGQE is illustrated in Figure 1. In the retrieval stage, given a corpus \(C=\{D_{1},D_{2},...D_{m}\}\), the task of each query \(q\) is to return a list \(R=\{p_{1},p_{2},...,p_{k}\}\) from \(C\) containing k (\(k<<C\)) most relevant passages to the query, i.e., the passages most likely to contain the correct answer for the current query. Then, top-\(k\) passages (we set \(k=3\) in the paper) were selected from the initial retrieval results \(R\) as reference materials and concatenated them with the initial query \(q\) as \([CLS]q[SEP]p_{1}...p_{k}[SEP]\), which is input into the expansion term generation model \(G\)(including \(G_{A}\), \(G_{S}\), \(G_{T}\)); Each query expansion generator generates the corresponding query expansion terms, namely \(G_{A}\), \(G_{S}\) and \(G_{T}\) generates \(ctx(A)\), \(ctx(S)\) and \(ctx(T)\), respectively; the three expansion terms, together with the initial query, form new queries \(q_{A}\), \(q_{S}\), and \(q_{T}\) to obtain their respective retrieval results \(R_{A}\), \(R_{S}\), and \(R_{T}\); Combine the retrieval results to obtain the final search result \(R\). Finally, the obtained retrieval results are sent to the reader to verify the performance of HTGQE on the OpenQA system. To further validate the effectiveness of HTGQE, we equip it with extractive and generative readers for end-to-end OpenQA evaluation. Notably, HTGQE first uses an initial query from a specific dataset to retrieve relevant passages as reference material for the generator. Next, three types of query expansion generators are trained on the specific dataset, and the results of these generators are used to expand the initial query. HTGQE only utilizes the retrieval results of the retriever and has nothing to do with the structure of the retriever or reader. Using a different structure for the retriever or reader does not affect the HTGQE process.  
Unlike GAR [10], which regards query as input. We combine PRF techniques from information retrieval when generating various contexts, transforming the closed-book generation model into an open-book generation model. Specifically, GAR only treats queries as input, and HTGQE takes the first three pseudo-relevant passages as input to the generated model. This not only reduces the difficulty of text generation but also  
Figure 1: The framework of HTGQE.  
significantly improves the quality of the generated text. Below, we introduce the meanings of the three contexts:  
\(ctx(A)\): Using the answer as query expansion term is valuable for retrieving passages containing the target. This is advantageous because even if the generated answers are only partially accurate or incorrect, the retriever can still retrieve the generated answers if they are related to (e.g., appear with) the passage containing the correct answer.  
\(ctx(S)\): Sentence containing the default target. Similar to employing the answer as the generation objective, such sentences can enhance the retrieval of relevant passages even if they lack the answer, as their semantics closely align with the question/answer context.  
\(ctx(T)\):Title of the target passage. Titles of relevant passages frequently represent notable entities and occasionally provide answers to queries.  
Although some generated query contexts may include unrealistic or non-factual contexts due to hallucinations in text generation and introduce noise during retrieval, they prove more advantageous than detrimental. Our experiments demonstrate that HTGQE significantly enhances retrieval and question-answering performance compared to BM25. Moreover, generating three different (complementary) query contexts and fusing their retrieval results further mitigated the impact of hallucinated content.