@inproceedings{zhou-etal-2025-ccnu,
title = "{CCNU} at {S}em{E}val-2025 Task 8: Enhancing Question Answering on Tabular Data with Two-Stage Corrections",
author = "Zhou, Chenlian and
Cai, Xilu and
Tong, Yajuan and
Wu, Chengzhao and
Xu, Xin and
Chen, Guanyi and
He, Tingting",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.115/",
pages = "841--845",
ISBN = "979-8-89176-273-2",
abstract = "We present the system developed by the Central China Normal University (CCNU) team for the SemEval-2025 shared task 8, which focuses on Question-Answering (QA) for tabular data. Our approach leverages multiple Large Language Models (LLMs), conducting tabular QA as code completion. Additionally, to improve its reliability, we introduce a two-stage corrections mechanism, in which we instruct the LLM to correct the code according to the judges of whether the code is executable and whether the answer obtained from executing the code is semantically consistent with the question. The experiment demonstrates that code correction works but answer correction does not. Finally, we discuss other unsuccessful approaches explored during our development process."
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%0 Conference Proceedings
%T CCNU at SemEval-2025 Task 8: Enhancing Question Answering on Tabular Data with Two-Stage Corrections
%A Zhou, Chenlian
%A Cai, Xilu
%A Tong, Yajuan
%A Wu, Chengzhao
%A Xu, Xin
%A Chen, Guanyi
%A He, Tingting
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F zhou-etal-2025-ccnu
%X We present the system developed by the Central China Normal University (CCNU) team for the SemEval-2025 shared task 8, which focuses on Question-Answering (QA) for tabular data. Our approach leverages multiple Large Language Models (LLMs), conducting tabular QA as code completion. Additionally, to improve its reliability, we introduce a two-stage corrections mechanism, in which we instruct the LLM to correct the code according to the judges of whether the code is executable and whether the answer obtained from executing the code is semantically consistent with the question. The experiment demonstrates that code correction works but answer correction does not. Finally, we discuss other unsuccessful approaches explored during our development process.
%U https://aclanthology.org/2025.semeval-1.115/
%P 841-845
Markdown (Informal)
[CCNU at SemEval-2025 Task 8: Enhancing Question Answering on Tabular Data with Two-Stage Corrections](https://aclanthology.org/2025.semeval-1.115/) (Zhou et al., SemEval 2025)
ACL