SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction
arXiv:2606.04691v1 Announce Type: new Abstract: Zero-shot information extraction (IE) with large language models (LLMs) has attracted increasing attention due to its flexibility in adapting to new schemas and domains without task-specific training. Existing approaches mainly rely on monolithic prompting, each-type prompting, or multi-agent debate. However, monolithic prompting often suffers from boundary and type errors, while each-type prompting and multi-agent debate introduce cross-type conflicts, redundant agent interactions, and substantial token overhead. To address these challenges, we
The paper addresses current limitations in zero-shot information extraction with LLMs, specifically inefficiency and errors in existing prompting methods, by proposing a new multi-agent framework.
Improving zero-shot information extraction directly enhances the practical utility and robustness of large language models for automated data processing across various domains.
This new framework could significantly reduce token costs and improve accuracy for automating complex information extraction tasks, making LLMs more reliable for enterprise applications.
- · AI developers
- · Businesses adopting LLM-based automation
- · Information extraction software providers
- · Traditional rule-based IE systems
- · Manual data entry services
More efficient and accurate information extraction will become widely accessible to enterprises.
This efficiency will accelerate the automation of knowledge work, impacting white-collar employment patterns.
The enhanced capability of LLMs to parse and organize unstructured data will create new industries around AI-driven content analysis and synthesis.
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Read at arXiv cs.CL