STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking
arXiv:2507.03674v3 Announce Type: replace Abstract: Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks. We introduce \textsc{StructSense}, a modular, task-agnostic, open-source framework that integrates ontology-guided symbolic knowledge, agentic self-evaluative refinement, and human-in-the-loop validation for robust domain-aware extraction. We evaluate \textsc{StructSense} on three tasks of increasing semantic
The increasing complexity and specialization of scientific literature necessitate more robust AI tools for information extraction, pushing development in hybrid AI approaches that combine symbolic knowledge with LLMs.
This framework offers a path to more reliable and generalizable AI for structured information extraction, critical for accelerating R&D and discovery in complex domains.
The introduction of a modular, task-agnostic framework with human-in-the-loop validation changes how large language models can be effectively deployed for specialized, domain-specific tasks without sacrificing accuracy or generalizability.
- · AI researchers and developers
- · Scientific research institutions
- · Data analysis software providers
- · Knowledge management platforms
- · Manual data extraction services
- · Generic LLM-only solutions for specialized tasks
Enhanced efficiency and accuracy in extracting structured data from scientific texts.
Faster research cycles and accelerated discovery in various scientific and technical fields.
The development of new AI-driven research methodologies for generating novel hypotheses from vast, structured datasets.
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Read at arXiv cs.CL