A Self-Reflective LLM-based Architecture for Semi-Open Event Extraction

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, medium

Summary

A new multi-agent reflective architecture for event extraction, based on generative large language models (LLMs), has been introduced. This system is the first to implement Semi-Open Event Extraction (SOEE), a hybrid framework that merges fixed event template fields with dynamically generated attributes. A key novel feature is its self-reflection capability, defined as the system's ability to generate questions about missing or implicit event information and then find answers internally. The architecture models event extraction as an iterative dialogue between a reflective LLM agent, which poses questions, and expert agents, which provide domain-aware answers and generate initial event templates. Evaluated in the health domain, the system demonstrated very promising results, confirming that LLM-based reflective multi-agent reasoning can accurately perform event extraction and creatively expand event templates.

Key takeaway

For NLP Engineers designing advanced event extraction systems, particularly in domains requiring flexible schema adaptation, you should investigate multi-agent reflective LLM architectures. This approach, featuring Semi-Open Event Extraction (SOEE) and self-reflection, allows for dynamic attribute generation and comprehensive template expansion. Consider implementing an iterative dialogue between reflective and expert LLM agents to uncover implicit information and enhance domain-aware extraction accuracy, moving beyond rigid, fixed-template systems.

Key insights

A multi-agent LLM architecture uses self-reflection and dynamic attribute generation for Semi-Open Event Extraction.

Principles

• LLM-based self-reflection enhances information discovery.
• Multi-agent dialogue improves domain-aware event extraction.
• Hybrid event templates combine structure with flexibility.

Method

Event extraction proceeds via an iterative dialogue: a reflective LLM agent generates questions for missing information, and expert agents provide domain-aware answers and initial event templates.

In practice

• Dynamic event template expansion.
• Improved information retrieval in specialized domains.
• Automated question generation for data gaps.

Topics

• Event Extraction
• Large Language Models
• Multi-Agent Systems
• Self-Reflection
• Semi-Open Event Extraction
• Health Domain AI

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.