# Harvesting Event Schemas from Large Language Models

arXiv: https://arxiv.org/abs/2305.07280 · HTML: https://mlsanity.com/paper/2305.07280/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 55 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [Linguistic Knowledge and Transferability of Contextual Representations](https://mlsanity.com/paper/1903.08855/) (2019)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [oLMpics -- On what Language Model Pre-training Captures](https://mlsanity.com/paper/1912.13283/) (2019)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [Explanations from Large Language Models Make Small Reasoners Better](https://mlsanity.com/paper/2210.06726/) (2022)
- [Evaluating Commonsense in Pre-trained Language Models](https://mlsanity.com/paper/1911.11931/) (2019)
- [What Language Model to Train if You Have One Million GPU Hours?](https://mlsanity.com/paper/2210.15424/) (2022)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [Open Domain Event Extraction Using Neural Latent Variable Models](https://mlsanity.com/paper/1906.06947/) (2019)
- [Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer](https://mlsanity.com/paper/2112.08995/) (2021)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [Probabilistic Frame Induction](https://mlsanity.com/paper/1302.4813/) (2013)
- [Open Relation and Event Type Discovery with Type Abstraction](https://mlsanity.com/paper/2212.00178/) (2022)
- [Joint Learning Templates and Slots for Event Schema Induction](https://mlsanity.com/paper/1603.01333/) (2016)
- [Unsupervised Semantic Frame Induction using Triclustering](https://mlsanity.com/paper/1805.04715/) (2018)

## Built on by (most cited first, 6 of 6)

- [LLM-DER:A Named Entity Recognition Method Based on Large Language Models for Chinese Coal Chemical Domain](https://mlsanity.com/paper/2409.10077/) (2024)
- [Causal Graph based Event Reasoning using Semantic Relation Experts](https://mlsanity.com/paper/2506.06910/) (2025)
- [PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation](https://mlsanity.com/paper/2307.00470/) (2023)
- [Induce, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning](https://mlsanity.com/paper/2506.13470/) (2025)
- [Scene-Aware Memory Discrimination: Deciding Which Personal Knowledge Stays](https://mlsanity.com/paper/2602.11607/) (2026)
- [ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation](https://mlsanity.com/paper/2602.15189/) (2026)

## Abstract (arXiv)

Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge. Unfortunately, it is challenging to automatically induce high-quality and high-coverage event schemas due to the open nature of real-world events, the diversity of event expressions, and the sparsity of event knowledge. In this paper, we propose a new paradigm for event schema induction -- knowledge harvesting from large-scale pre-trained language models, which can effectively resolve the above challenges by discovering, conceptualizing and structuralizing event schemas from PLMs. And an Event Schema Harvester (ESHer) is designed to automatically induce high-quality event schemas via in-context generation-based conceptualization, confidence-aware schema structuralization and graph-based schema aggregation. Empirical results show that ESHer can induce high-quality and high-coverage event schemas on varying domains.