@inproceedings{kang-etal-2026-r,
    title = "{R}-{GDA}: Reflective Guidance Data Augmentation with Multi-Agent Feedback for Domain-Specific Named Entity Recognition",
    author = "Kang, Hyeonseok  and
      Namgoong, Hyuk  and
      Pyeon, Goun  and
      Jung, Sangkeun",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.259/",
    pages = "4938--4953",
    ISBN = "979-8-89176-386-9",
    abstract = "Domain-specific Named Entity Recognition (NER) often requires data augmentation due to the scarcity of annotated corpora. Guidance Data Augmentation (GDA), a method utilizing Large Language Models (LLMs) to decompose sentences into abstract components, can lead to over-abstraction, resulting in undefined entity tags and sentences lacking domain-specific vocabulary. In this work, we propose Reflective GDA (R-GDA), a framework that introduces a multi-agent feedback loop to enhance augmentation quality. R-GDA incorporates two distinct agents: a **Guidance Refiner (GR)**, which assesses the initial abstraction to prevent over-generalization, and an **Augmentation Calibrator (AC)**, which validates the final generated sample for domain-fidelity and tag integrity. On the SciERC and NCBI-disease datasets, R-GDA improves F1-Score, validating its effectiveness. Concurrently, it achieves low BERTScore in most cases, indicating greater sentence diversity. For the FIN dataset, it achieves performance comparable to the GDA baseline. R-GDA consistently prevents errors regarding domain-specific tags, demonstrating that the reflective feedback mechanism enhances data fidelity by mitigating critical generation errors."
}