# Zero-Shot On-the-Fly Event Schema Induction

arXiv: https://arxiv.org/abs/2210.06254 · HTML: https://mlsanity.com/paper/2210.06254/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 85 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020)
- [Zero-Shot Transfer Learning for Event Extraction](https://mlsanity.com/paper/1707.01066/) (2017)
- [Reasoning about Goals, Steps, and Temporal Ordering with WikiHow](https://mlsanity.com/paper/2009.07690/) (2020)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Temporal Reasoning on Implicit Events from Distant Supervision](https://mlsanity.com/paper/2010.12753/) (2020)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [Temporal Common Sense Acquisition with Minimal Supervision](https://mlsanity.com/paper/2005.04304/) (2020)
- [An Improved Neural Baseline for Temporal Relation Extraction](https://mlsanity.com/paper/1909.00429/) (2019)
- [Unsupervised Label-aware Event Trigger and Argument Classification](https://mlsanity.com/paper/2012.15243/) (2020)
- [Analogous Process Structure Induction for Sub-event Sequence Prediction](https://mlsanity.com/paper/2010.08525/) (2020)
- [Causal Inference of Script Knowledge](https://mlsanity.com/paper/2004.01174/) (2020)
- [Event Representations with Tensor-based Compositions](https://mlsanity.com/paper/1711.07611/) (2017)
- [Probabilistic Frame Induction](https://mlsanity.com/paper/1302.4813/) (2013)
- [Joint Learning Templates and Slots for Event Schema Induction](https://mlsanity.com/paper/1603.01333/) (2016)
- [Pairwise Representation Learning for Event Coreference](https://mlsanity.com/paper/2010.12808/) (2020)
- [Learning Constraints and Descriptive Segmentation for Subevent Detection](https://mlsanity.com/paper/2109.06316/) (2021)
- [Two Discourse Driven Language Models for Semantics](https://mlsanity.com/paper/1606.05679/) (2016)
- [Mining Logical Event Schemas From Pre-Trained Language Models](https://mlsanity.com/paper/2204.05939/) (2022)

## Built on by (most cited first, 9 of 9)

- [Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning](https://mlsanity.com/paper/2305.16646/) (2023)
- [Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification](https://mlsanity.com/paper/2307.01972/) (2023)
- [Devil's Advocate: Anticipatory Reflection for LLM Agents](https://mlsanity.com/paper/2405.16334/) (2024)
- [Human-in-the-Loop Schema Induction](https://mlsanity.com/paper/2302.13048/) (2023)
- [Drafting Event Schemas using Language Models](https://mlsanity.com/paper/2305.14847/) (2023)
- [LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research](https://mlsanity.com/paper/2509.24294/) (2025)
- [We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses](https://mlsanity.com/paper/2310.06245/) (2023)
- [Generative Induction of Dialogue Task Schemas with Streaming Refinement and Simulated Interactions](https://mlsanity.com/paper/2504.18474/) (2025)
- [Event Causality Identification with Synthetic Control](https://mlsanity.com/paper/2509.18156/) (2025)

## Abstract (arXiv)

What are the events involved in a pandemic outbreak? What steps should be taken when planning a wedding? The answers to these questions can be found by collecting many documents on the complex event of interest, extracting relevant information, and analyzing it. We present a new approach in which large language models are utilized to generate source documents that allow predicting, given a high-level event definition, the specific events, arguments, and relations between them to construct a schema that describes the complex event in its entirety. Using our model, complete schemas on any topic can be generated on-the-fly without any manual data collection, i.e., in a zero-shot manner. Moreover, we develop efficient methods to extract pertinent information from texts and demonstrate in a series of experiments that these schemas are considered to be more complete than human-curated ones in the majority of examined scenarios. Finally, we show that this framework is comparable in performance with previous supervised schema induction methods that rely on collecting real texts while being more general and flexible without the need for a predefined ontology.