@inproceedings{nararatwong-etal-2025-fin,
title = "Fin-{DBQA} Shared-task: Database Querying and Reasoning",
author = "Nararatwong, Rungsiman and
Kertkeidkachorn, Natthawut and
Takamura, Hiroya and
Ichise, Ryutaro",
editor = "Chen, Chung-Chi and
Moreno-Sandoval, Antonio and
Huang, Jimin and
Xie, Qianqian and
Ananiadou, Sophia and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.finnlp-1.44/",
pages = "385--391",
abstract = "This paper presents the results of the Fin-DBQA shared task based on a question-answering dataset, focusing on database querying and reasoning. The dataset, consisting of 400 questions grouped into 40 conversations, evaluates language models' abilities to answer sequential questions with complex reasoning and multi-hop queries in a multi-turn conversational question-answering setting. Each sample includes the question, answer, database queries, querying result (tables), and a program (series of operations) that produces the answer from the result. We received 52 submissions from three participants, with scores significantly surpassing the baselines. One participant submitted a paper detailing a prompt-based solution using large language models with additional data preprocessing that helps improve the overall performance."
}
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%0 Conference Proceedings
%T Fin-DBQA Shared-task: Database Querying and Reasoning
%A Nararatwong, Rungsiman
%A Kertkeidkachorn, Natthawut
%A Takamura, Hiroya
%A Ichise, Ryutaro
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F nararatwong-etal-2025-fin
%X This paper presents the results of the Fin-DBQA shared task based on a question-answering dataset, focusing on database querying and reasoning. The dataset, consisting of 400 questions grouped into 40 conversations, evaluates language models’ abilities to answer sequential questions with complex reasoning and multi-hop queries in a multi-turn conversational question-answering setting. Each sample includes the question, answer, database queries, querying result (tables), and a program (series of operations) that produces the answer from the result. We received 52 submissions from three participants, with scores significantly surpassing the baselines. One participant submitted a paper detailing a prompt-based solution using large language models with additional data preprocessing that helps improve the overall performance.
%U https://aclanthology.org/2025.finnlp-1.44/
%P 385-391
Markdown (Informal)
[Fin-DBQA Shared-task: Database Querying and Reasoning](https://aclanthology.org/2025.finnlp-1.44/) (Nararatwong et al., FinNLP 2025)
ACL
- Rungsiman Nararatwong, Natthawut Kertkeidkachorn, Hiroya Takamura, and Ryutaro Ichise. 2025. Fin-DBQA Shared-task: Database Querying and Reasoning. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 385–391, Abu Dhabi, UAE. Association for Computational Linguistics.