# A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering
## 4 Discussion  
HTGQE can also be improved in the following aspects: (1) Relevant reference materials. HTGQE combines the PRF techniques in information retrieval and the text generation task to provide reference materials, transforming the closed-book task into an open-book task, making the generation of targets easier and more accurate. In addition to utilizing the content in the retrieval results, the reference materials can also use any useful knowledge, such as knowledge base content related to the query terms, web knowledge, chart content, etc., as long as it provides the correct guidance for the text generation task. (2) Various contextual information. Regarding the generation targets, this paper mainly generates three kinds of query expansion contexts such as GAR [10]: the answer corresponding to the query, the sentence containing the answer, and the title of the passage containing the query.  
\begin{table}
\begin{tabular}{l l} \hline \hline case1 & **query:** who started the trojan war in the iliad? \\  & \(ctx(A)\)**:** Paris [[Paris of Troy]] \\  & \(ctx(S)\)**:** the Trojan War was started by the **Acheaans (Greeks)**. [In Greek mythology, the Trojan War was waged against the city of Troy by the Achaeans (Greeks) after Paris of Troy took [PERSON] from her husband [PERSON].] \\  & \(ctx(T)\)**: Trojan War** [Helen of Troy (film)] \\ \hline case2 & **query:** what is the european recovery program of 1947 better known as? \\  & \(ctx(A)\)**: European Recovery Plan** [[The Marshall Plan]] \\  & \(ctx(S)\)**: The Marshall Plan, officially the European Recovery Program. ( \\  & born November 26, 1962 ) is an American country music singer. [The Marshall Plan was an American initiative to aid Western Europe, in which the United States gave over \$13 billion in economic support to help rebuild Western European economies after the end of World War II.] \\  & \(ctx(T)\)**: as Marshall Plan [SEP] Committee for the Marshall Plan [SEP] I Do ([PERSON] album)** [Committee of European Economic Co-operation [SEP] Federal Ministry of Matters of the Marshall Plan.] \\ \hline case3 & **query:** who is the original singer of and i am telling you? \\  & \(ctx(A)\)**: [PERSON]** [[[PERSON]]] \\  & \(ctx(S)\)**: [PERSON] for its-release in 1982 for which it became a number - one R\&B hit for [PERSON]** [[PERSON] originated the role on Broadway in 1981 and won a Tony Award for her performance as well as the Grammy for Best R\&B Performance.] \\  & \(ctx(T)\)**: And I Am Telling You I’m Not Going** [I Am Changing [SEP] [PERSON] [SEP] [PERSON] [SEP] Dreamgirls] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Examples of generated query contexts. Ground-truth references are shown in the [braces]. Incorrect predictions are shown in red. Correct predictions are shown in green.  
Of course, many other expansion contexts can improve the retrieval performance, such as generating the background knowledge of the queries or directly generating multiple context information related to the query, ranking these contexts, and selecting the most relevant ones as query expansion terms. This issue is worth further exploration. (3) Fusion methods. Additionally, the fusion methods of individual retrieval results still require in-depth investigation. The fusion approach used in this paper is rather simplistic and crude; in the future, we will attempt various fusion techniques further to enhance the retrieval stage and OpenQA system performance. (4) Generative models. Furthermore, with the increasing popularity of the ChatGPT model, the potential of generative models has been demonstrated. Therefore, it is worth exploring more query expansion methods based on generative models in the future.