@inproceedings{zheng-etal-2025-logical,
    title = "Logical {DA}: Enhancing Data Augmentation for Logical Reasoning via a Multi-Agent System",
    author = "Zheng, Haoqi  and
      Wang, Dong  and
      Yang, Silin  and
      Qi, Yunpeng  and
      Jin, Ruochun  and
      Xu, Liyang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.356/",
    doi = "10.18653/v1/2025.findings-acl.356",
    pages = "6843--6855",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent advancements in large language models (LLMs) have highlighted the importance of improving their reasoning capabilities. A critical challenge lies in the scarcity of high-quality reasoning data{---}characterized by diversity and rich supervisory signals{---}necessary for robust model training. While data augmentation (DA) methods have been leveraged to mitigate this scarcity, prevailing approaches often introduce noise and exhibit logical inconsistencies, thereby diminishing their utility for complex reasoning tasks. Moreover, existing DA paradigms predominantly isolate data synthesis from label validation, failing to unify these complementary processes within a cohesive architecture.To address these limitations, we introduce Logical DA, a multi-agent framework for enhancing reasoning-focused data augmentation in few-shot learning scenarios. Our system includes four agents operating through two synergistic phases: (1) diverse data generation, and (2) label verification.The system incorporates a reflection mechanism to continuously improve data quality by leveraging feedback from logical validation. We demonstrate the effectiveness of Logical DA through experiments on various tasks and datasets, achieving the highest average improvement in task accuracy in both fine-tuning and in-context learning paradigms, with an average improvement of 7.61{\%} when applied to fine-tuning."
}