STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking

• URL: http://arxiv.org/abs/2507.03674v1
• Date: Fri, 04 Jul 2025 15:51:07 GMT
• Title: STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking
• Authors: Tek Raj Chhetri, Yibei Chen, Puja Trivedi, Dorota Jarecka, Saif Haobsh, Patrick Ray, Lydia Ng, Satrajit S. Ghosh,
• Abstract summary: StructSense is a modular, task-agnostic, open-source framework for structured information extraction built on Large Language Models.<n>It is guided by domain-specific symbolic knowledge enabling it encoded complex domain content effectively.<n>We demonstrate that StructSense can overcome both the limitations of domain sensitivity and the lack of cross-task generalizability.
• Score: 2.355572228890207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to extract structured information from unstructured sources-such as free-text documents and scientific literature-is critical for accelerating scientific discovery and knowledge synthesis. Large Language Models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks, including structured information extraction. However, their effectiveness often diminishes in specialized, domain-specific contexts that require nuanced understanding and expert-level domain knowledge. In addition, existing LLM-based approaches frequently exhibit poor transferability across tasks and domains, limiting their scalability and adaptability. To address these challenges, we introduce StructSense, a modular, task-agnostic, open-source framework for structured information extraction built on LLMs. StructSense is guided by domain-specific symbolic knowledge encoded in ontologies, enabling it to navigate complex domain content more effectively. It further incorporates agentic capabilities through self-evaluative judges that form a feedback loop for iterative refinement, and includes human-in-the-loop mechanisms to ensure quality and validation. We demonstrate that StructSense can overcome both the limitations of domain sensitivity and the lack of cross-task generalizability, as shown through its application to diverse neuroscience information extraction tasks.

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