Technology Category
Natural Language Processing: Information ExtractionHumans and AI: Human-Aware Planning and Behavior PredictionMachine Learning: Large Multimodal Models (LMMs)
Extracting Events Like Code: A Multi-Agent Programming Framework for Zero-Shot Event Extraction
🤖 AI Summary
Zero-shot event extraction (ZSEE) faces critical challenges including structural incompleteness and schema violations. To address these, we propose the “Event-as-Code” paradigm, which compiles event schemas into executable class definitions, and design a multi-agent collaborative framework that decouples the process into four sequential, iterative phases: retrieval, planning, code generation, and validation—enabling deterministic structural verification and refinement. Leveraging large language models (LLMs), our approach integrates schema-aware prompting, dynamic feedback loops, and a dedicated validation agent to ensure high-fidelity, structured extraction. Extensive experiments across five domains and six state-of-the-art LLMs demonstrate that our method significantly outperforms existing zero-shot baselines, achieving superior performance in trigger identification, argument filling, and schema consistency—yielding outputs that are more complete, accurate, and strictly compliant with target event schemas.
Technology Category
Application Category
📝 Abstract
Zero-shot event extraction (ZSEE) remains a significant challenge for large language models (LLMs) due to the need for complex reasoning and domain-specific understanding. Direct prompting often yields incomplete or structurally invalid outputs--such as misclassified triggers, missing arguments, and schema violations. To address these limitations, we present Agent-Event-Coder (AEC), a novel multi-agent framework that treats event extraction like software engineering: as a structured, iterative code-generation process. AEC decomposes ZSEE into specialized subtasks--retrieval, planning, coding, and verification--each handled by a dedicated LLM agent. Event schemas are represented as executable class definitions, enabling deterministic validation and precise feedback via a verification agent. This programming-inspired approach allows for systematic disambiguation and schema enforcement through iterative refinement. By leveraging collaborative agent workflows, AEC enables LLMs to produce precise, complete, and schema-consistent extractions in zero-shot settings. Experiments across five diverse domains and six LLMs demonstrate that AEC consistently outperforms prior zero-shot baselines, showcasing the power of treating event extraction like code generation. The code and data are released on https://github.com/UESTC-GQJ/Agent-Event-Coder.
Problem
Research questions and friction points this paper is trying to address.
Zero-shot event extraction faces incomplete outputs from direct LLM prompting
Existing methods produce misclassified triggers and schema violations
Complex reasoning requires structured approach for domain-specific understanding
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-agent framework decomposes event extraction tasks
Event schemas represented as executable class definitions
Iterative refinement through collaborative agent workflows
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