@inproceedings{bao-etal-2024-abstract,
title = "{A}bstract {M}eaning {R}epresentation-Based Logic-Driven Data Augmentation for Logical Reasoning",
author = "Bao, Qiming and
Peng, Alex and
Deng, Zhenyun and
Zhong, Wanjun and
Gendron, Gael and
Pistotti, Timothy and
Tan, Neset and
Young, Nathan and
Chen, Yang and
Zhu, Yonghua and
Denny, Paul and
Witbrock, Michael and
Liu, Jiamou",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.353",
doi = "10.18653/v1/2024.findings-acl.353",
pages = "5914--5934",
abstract = "Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering reliable data from the web to build comprehensive training datasets, subsequently affecting performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logical structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into text to create augmented data. Notably, our methodology is architecture-agnostic and enhances both generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as reading comprehension requiring logical reasoning, textual entailment, and natural language inference. Furthermore, our method leads on the ReClor leaderboard. The source code and data are publicly available",
}
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<abstract>Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering reliable data from the web to build comprehensive training datasets, subsequently affecting performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logical structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into text to create augmented data. Notably, our methodology is architecture-agnostic and enhances both generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as reading comprehension requiring logical reasoning, textual entailment, and natural language inference. Furthermore, our method leads on the ReClor leaderboard. The source code and data are publicly available</abstract>
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%0 Conference Proceedings
%T Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
%A Bao, Qiming
%A Peng, Alex
%A Deng, Zhenyun
%A Zhong, Wanjun
%A Gendron, Gael
%A Pistotti, Timothy
%A Tan, Neset
%A Young, Nathan
%A Chen, Yang
%A Zhu, Yonghua
%A Denny, Paul
%A Witbrock, Michael
%A Liu, Jiamou
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F bao-etal-2024-abstract
%X Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering reliable data from the web to build comprehensive training datasets, subsequently affecting performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logical structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into text to create augmented data. Notably, our methodology is architecture-agnostic and enhances both generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as reading comprehension requiring logical reasoning, textual entailment, and natural language inference. Furthermore, our method leads on the ReClor leaderboard. The source code and data are publicly available
%R 10.18653/v1/2024.findings-acl.353
%U https://aclanthology.org/2024.findings-acl.353
%U https://doi.org/10.18653/v1/2024.findings-acl.353
%P 5914-5934
Markdown (Informal)
[Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning](https://aclanthology.org/2024.findings-acl.353) (Bao et al., Findings 2024)
ACL
- Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, and Jiamou Liu. 2024. Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5914–5934, Bangkok, Thailand. Association for Computational Linguistics.
