DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning
Rungsiman Nararatwong, Chung-Chi Chen, Natthawut Kertkeidkachorn, Hiroya Takamura, Ryutaro Ichise
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.- Anthology ID:
- 2024.findings-acl.900
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15169–15182
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.900
- DOI:
- 10.18653/v1/2024.findings-acl.900
- Cite (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.
- Cite (Informal):
- DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning (Nararatwong et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.findings-acl.900.pdf