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    <abstract>This paper introduces the Database Querying and Reasoning Dataset for Question Answering (DBQR-QA), aimed at addressing the gap in current question-answering (QA) research by emphasizing the essential processes of database querying and reasoning to answer questions. Specifically designed to accommodate sequential questions and multi-hop queries, DBQR-QA more accurately mirrors the dynamics of real-world information retrieval and analysis, with a particular focus on the financial reports of US companies. The dataset’s construction, the challenges encountered during its development, the performance of large language models on this dataset, and a human evaluation are thoroughly discussed to illustrate the dataset’s complexity and highlight future research directions in querying and reasoning tasks.</abstract>
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%0 Conference Proceedings
%T DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning
%A Nararatwong, Rungsiman
%A Chen, Chung-Chi
%A Kertkeidkachorn, Natthawut
%A Takamura, Hiroya
%A Ichise, Ryutaro
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F nararatwong-etal-2024-dbqr
%X This paper introduces the Database Querying and Reasoning Dataset for Question Answering (DBQR-QA), aimed at addressing the gap in current question-answering (QA) research by emphasizing the essential processes of database querying and reasoning to answer questions. Specifically designed to accommodate sequential questions and multi-hop queries, DBQR-QA more accurately mirrors the dynamics of real-world information retrieval and analysis, with a particular focus on the financial reports of US companies. The dataset’s construction, the challenges encountered during its development, the performance of large language models on this dataset, and a human evaluation are thoroughly discussed to illustrate the dataset’s complexity and highlight future research directions in querying and reasoning tasks.
%U https://aclanthology.org/2024.findings-acl.900
%P 15169-15182

Markdown (Informal)

[DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning](https://aclanthology.org/2024.findings-acl.900) (Nararatwong et al., Findings 2024)

• DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning (Nararatwong et al., Findings 2024)

ACL

• Rungsiman Nararatwong, Chung-Chi Chen, Natthawut Kertkeidkachorn, Hiroya Takamura, and Ryutaro Ichise. 2024. DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning. In Findings of the Association for Computational Linguistics ACL 2024, pages 15169–15182, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.