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        <title>Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering</title>
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    <abstract>Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models.</abstract>
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%0 Conference Proceedings
%T Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering
%A Lin, Jiuheng
%A Lai, Yuxuan
%A Feng, Yansong
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F lin-etal-2024-chain
%X Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models.
%U https://aclanthology.org/2024.findings-emnlp.968
%P 16596-16611

Markdown (Informal)

[Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering](https://aclanthology.org/2024.findings-emnlp.968) (Lin et al., Findings 2024)

• Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering (Lin et al., Findings 2024)

ACL

• Jiuheng Lin, Yuxuan Lai, and Yansong Feng. 2024. Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 16596–16611, Miami, Florida, USA. Association for Computational Linguistics.