Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914963970326528 |
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| author | Lin, Jiuheng Lai, Yuxuan Feng, Yansong |
| author_facet | Lin, Jiuheng Lai, Yuxuan Feng, Yansong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05442 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering Lin, Jiuheng Lai, Yuxuan Feng, Yansong Computation and Language 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. |
| title | Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.05442 |