mlsanityData to 7 Oct 2026

Human-in-the-Loop Schema Induction

arXiv 2302.13048 ↗First posted 25 Feb 2023cs.HC cs.CLcited by 13

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Abstract (arXiv)

Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction(IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 12 of its 34 references that are in the map. First posted on arXiv 25 Feb 2023.

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12 of 34 references.

References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  5. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  6. 2019Simple BERT Models for Relation Extraction and Semantic Role Labelingcited by 154
  7. 2018Higher-order Coreference Resolution with Coarse-to-fine Inferencecited by 150
  8. 2018Event2Mind: Commonsense Inference on Events, Intents, and Reactionscited by 82
  9. 2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 39
  10. 2022Zero-Shot On-the-Fly Event Schema Inductioncited by 9
  11. 2016Two Discourse Driven Language Models for Semanticscited by 8
  12. 2021Machine-Assisted Script Curationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 2; 2025: 4; 2026: 1.

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  1. 2023Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoningcited by 44
  2. 2025NarraBench: A Comprehensive Framework for Narrative Benchmarkingcited by 13
  3. 2024SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptionscited by 12
  4. 2023OpenPI2.0: An Improved Dataset for Entity Tracking in Textscited by 8
  5. 2023Drafting Event Schemas using Language Modelscited by 6
  6. 2025How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyondcited by 4
  7. 2023Choice-75: A Dataset on Decision Branching in Script Learningcited by 2
  8. 2023Large Language Models to the Rescue: Reducing the Complexity in Scientific Workflow Development Using ChatGPTcited by 2
  9. 2025HugAgent: A Human Simulation Benchmark for Individual-Level Reasoningcited by 2
  10. 2023RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editorcited by 1
  11. 2024MEEL: Multi-Modal Event Evolution Learningcited by 1
  12. 2025InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedbackcited by 1
1 more
  1. 2026DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysiscited by 0