@inproceedings{pan-etal-2026-craft,
title = "{CRAFT}: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verification",
author = "Pan, Chenshuo and
Zhao, Yu and
Zhang, Jie and
Pan, Changzai and
Liang, Jiayi and
Mao, Yujie and
Wu, Zhenhe and
Song, Shuangyong and
Li, Yongxiang and
He, Zhongjiang",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.176/",
pages = "3934--3959",
ISBN = "979-8-89176-439-2",
abstract = "Table reasoning remains challenging for large language models (LLMs), particularly in tasks that require multi-step inference over long and structured tables. Existing approaches predominantly rely on single-direction reasoning, which limits their ability to explore alternative hypotheses across tasks. In this work, we propose CRAFT, a unified Counterfactual Reasoning Framework that reformulates Tabular question answering and fact verification into a general bidirectional verification process. Our method explicitly constructs both declarative statements and their counterfactual variants. Evidence is then extracted from reasoning along both the original and counterfactual paths, and integrated via a weighted mechanism to arrive at the final answer. Experimental results show that our approach consistently surpasses representative baselines on table reasoning datasets such as WikiTQ and TabFact, achieving especially large improvements on complex question answering. Our framework also significantly mitigates performance gaps between different backbone LLMs. This indicates that counterfactual reasoning effectively overcomes the limitations of single-direction inference, guiding LLMs toward more discerning reasoning and establishing a more principled paradigm for structured reasoning tasks."
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<abstract>Table reasoning remains challenging for large language models (LLMs), particularly in tasks that require multi-step inference over long and structured tables. Existing approaches predominantly rely on single-direction reasoning, which limits their ability to explore alternative hypotheses across tasks. In this work, we propose CRAFT, a unified Counterfactual Reasoning Framework that reformulates Tabular question answering and fact verification into a general bidirectional verification process. Our method explicitly constructs both declarative statements and their counterfactual variants. Evidence is then extracted from reasoning along both the original and counterfactual paths, and integrated via a weighted mechanism to arrive at the final answer. Experimental results show that our approach consistently surpasses representative baselines on table reasoning datasets such as WikiTQ and TabFact, achieving especially large improvements on complex question answering. Our framework also significantly mitigates performance gaps between different backbone LLMs. This indicates that counterfactual reasoning effectively overcomes the limitations of single-direction inference, guiding LLMs toward more discerning reasoning and establishing a more principled paradigm for structured reasoning tasks.</abstract>
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%0 Conference Proceedings
%T CRAFT: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verification
%A Pan, Chenshuo
%A Zhao, Yu
%A Zhang, Jie
%A Pan, Changzai
%A Liang, Jiayi
%A Mao, Yujie
%A Wu, Zhenhe
%A Song, Shuangyong
%A Li, Yongxiang
%A He, Zhongjiang
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F pan-etal-2026-craft
%X Table reasoning remains challenging for large language models (LLMs), particularly in tasks that require multi-step inference over long and structured tables. Existing approaches predominantly rely on single-direction reasoning, which limits their ability to explore alternative hypotheses across tasks. In this work, we propose CRAFT, a unified Counterfactual Reasoning Framework that reformulates Tabular question answering and fact verification into a general bidirectional verification process. Our method explicitly constructs both declarative statements and their counterfactual variants. Evidence is then extracted from reasoning along both the original and counterfactual paths, and integrated via a weighted mechanism to arrive at the final answer. Experimental results show that our approach consistently surpasses representative baselines on table reasoning datasets such as WikiTQ and TabFact, achieving especially large improvements on complex question answering. Our framework also significantly mitigates performance gaps between different backbone LLMs. This indicates that counterfactual reasoning effectively overcomes the limitations of single-direction inference, guiding LLMs toward more discerning reasoning and establishing a more principled paradigm for structured reasoning tasks.
%U https://aclanthology.org/2026.aacl-long.176/
%P 3934-3959
Markdown (Informal)
[CRAFT: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verification](https://aclanthology.org/2026.aacl-long.176/) (Pan et al., AACL-IJCNLP 2026)
ACL
- Chenshuo Pan, Yu Zhao, Jie Zhang, Changzai Pan, Jiayi Liang, Yujie Mao, Zhenhe Wu, Shuangyong Song, Yongxiang Li, and Zhongjiang He. 2026. CRAFT: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verification. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3934–3959, Hengqin, China. Association for Computational Linguistics.