DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning

Tanmay Parekh, Kartik Mehta, Ninareh Mehrabi, Kai-Wei Chang, and Nanyun Peng, in EMNLP, 2025.

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Abstract

Zero-shot event detection (ED) identifies event mentions in text without training data, but large language models struggle with complex ontologies and structural constraints. This paper proposes DiCoRe, a divergent-convergent reasoning framework that decouples ED using two modules: a Dreamer that encourages open-ended event discovery to boost coverage and a Grounder that uses finite-state-machine-guided decoding to align predictions with task-specific constraints. An LLM-based judge verifies outputs. Experiments across six datasets, five domains and nine models show that DiCoRe consistently outperforms zero-shot, transfer learning, and reasoning baselines, achieving 4-7% average F1 gains and establishing DiCoRe as a strong zero-shot ED framework.

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@inproceedings{parekh2025dicore,
  title = {DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning},
  author = {Parekh, Tanmay and Mehta, Kartik and Mehrabi, Ninareh and Chang, Kai-Wei and Peng, Nanyun},
  booktitle = {EMNLP},
  year = {2025}
}