mlsanityData to 14 Oct 2026

Interscript: A dataset for interactive learning of scripts through error feedback

arXiv 2112.07867 ↗First posted 15 Dec 2021cs.AIcited by 9

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Abstract (arXiv)

How can an end-user provide feedback if a deployed structured prediction model generates inconsistent output, ignoring the structural complexity of human language? This is an emerging topic with recent progress in synthetic or constrained settings, and the next big leap would require testing and tuning models in real-world settings. We present a new dataset, Interscript, containing user feedback on a deployed model that generates complex everyday tasks. Interscript contains 8,466 data points -- the input is a possibly erroneous script and a user feedback, and the output is a modified script. We posit two use-cases of ours that might significantly advance the state-of-the-art in interactive learning. The dataset is available at: https://github.com/allenai/interscript.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

14 of 30 references.

References · 14

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  3. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  4. 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 783
  5. 2020Transformers as Soft Reasoners over Languagecited by 270
  6. 2021TEACh: Task-driven Embodied Agents that Chatcited by 197
  7. 2020Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedbackcited by 44
  8. 2016Learning Language Games through Interactioncited by 43
  9. 2019Active Learning for Graph Neural Networks via Node Feature Propagationcited by 37
  10. 2021NL-EDIT: Correcting semantic parse errors through natural language interactioncited by 33
  11. 2020Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledgecited by 24
  12. 2021proScript: Partially Ordered Scripts Generation via Pre-trained Language Modelscited by 21
2 more
  1. 2020Active Imitation Learning with Noisy Guidancecited by 15
  2. 2019Improving Natural Language Interaction with Robots Using Advicecited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 2; 2025: 1; 2026: 1.

9 papers.

  1. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  2. 2024OpenCodeInterpreter: Integrating Code Generation with Execution and Refinementcited by 247
  3. 2023RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputscited by 105
  4. 2025A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoningcited by 40
  5. 2024Large Language Models Can Self-Correct with Key Condition Verificationcited by 39
  6. 2023CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Toolscited by 29
  7. 2023SCREWS: A Modular Framework for Reasoning with Revisionscited by 16
  8. 2022InterFair: Debiasing with Natural Language Feedback for Fair Interpretable Predictionscited by 7
  9. 2026Beyond Output Critique: Self-Correction via Task Distillationcited by 0