{"ID":2840460,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13118","arxiv_id":"2511.13118","title":"Extracting Events Like Code: A Multi-Agent Programming Framework for Zero-Shot Event Extraction","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.","short_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....","url_abs":"https://arxiv.org/abs/2511.13118","url_pdf":"https://arxiv.org/pdf/2511.13118v1","authors":"[\"Quanjiang Guo\",\"Sijie Wang\",\"Jinchuan Zhang\",\"Ben Zhang\",\"Zhao Kang\",\"Ling Tian\",\"Ke Yan\"]","published":"2025-11-17T08:17:15Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":606971,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2840460,"paper_url":"https://arxiv.org/abs/2511.13118","paper_title":"Extracting Events Like Code: A Multi-Agent Programming Framework for Zero-Shot Event Extraction","repo_url":"https://github.com/UESTC-GQJ/Agent-Event-Coder","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}