@inproceedings{li-etal-2025-aid,
    title = "{AID}-Agent: An {LLM}-Agent for Advanced Extraction and Integration of Documents",
    author = "Li, Bin  and
      Conen, Jannis  and
      Aller, Felix",
    editor = "Kamalloo, Ehsan  and
      Gontier, Nicolas  and
      Lu, Xing Han  and
      Dziri, Nouha  and
      Murty, Shikhar  and
      Lacoste, Alexandre",
    booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.realm-1.6/",
    doi = "10.18653/v1/2025.realm-1.6",
    pages = "80--88",
    ISBN = "979-8-89176-264-0",
    abstract = "Extracting structured information from complex unstructured documents is an essential but challenging task in today{'}s industrial applications. Complex document content, e.g., irregular table layout, and cross-referencing, can lead to unexpected failures in classical extractors based on Optical Character Recognition (OCR) or Large Language Models (LLMs). In this paper, we propose the AID-agent framework that synergistically integrates OCR with LLMs to enhance text processing capabilities. Specifically, the AID-agent maintains a customizable toolset, which not only provides external processing tools for complex documents but also enables customization for domain and task-specific tool requirements. In the empirical validation on a real-world use case, the proposed AID-agent demonstrates superior performance compared to conventional OCR and LLM-based approaches."
}