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    <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.</abstract>
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%0 Conference Proceedings
%T AID-Agent: An LLM-Agent for Advanced Extraction and Integration of Documents
%A Li, Bin
%A Conen, Jannis
%A Aller, Felix
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Lu, Xing Han
%Y Dziri, Nouha
%Y Murty, Shikhar
%Y Lacoste, Alexandre
%S Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-264-0
%F li-etal-2025-aid
%X 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.
%R 10.18653/v1/2025.realm-1.6
%U https://aclanthology.org/2025.realm-1.6/
%U https://doi.org/10.18653/v1/2025.realm-1.6
%P 80-88

Markdown (Informal)

[AID-Agent: An LLM-Agent for Advanced Extraction and Integration of Documents](https://aclanthology.org/2025.realm-1.6/) (Li et al., REALM 2025)

• AID-Agent: An LLM-Agent for Advanced Extraction and Integration of Documents (Li et al., REALM 2025)

ACL

• Bin Li, Jannis Conen, and Felix Aller. 2025. AID-Agent: An LLM-Agent for Advanced Extraction and Integration of Documents. In Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025), pages 80–88, Vienna, Austria. Association for Computational Linguistics.