mlsanityData to 7 Oct 2026

GenNI: Human-AI Collaboration for Data-Backed Text Generation

arXiv 2110.10185 ↗First posted 19 Oct 2021cs.CL cs.AI cs.HCcited by 11

Not on the map. Why?

Abstract (arXiv)

Table2Text systems generate textual output based on structured data utilizing machine learning. These systems are essential for fluent natural language interfaces in tools such as virtual assistants; however, left to generate freely these ML systems often produce misleading or unexpected outputs. GenNI (Generation Negotiation Interface) is an interactive visual system for high-level human-AI collaboration in producing descriptive text. The tool utilizes a deep learning model designed with explicit control states. These controls allow users to globally constrain model generations, without sacrificing the representation power of the deep learning models. The visual interface makes it possible for users to interact with AI systems following a Refine-Forecast paradigm to ensure that the generation system acts in a manner human users find suitable. We report multiple use cases on two experiments that improve over uncontrolled generation approaches, while at the same time providing fine-grained control. A demo and source code are available at https://genni.vizhub.ai .

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

35 of 63 references.

References · 35

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2013Auto-Encoding Variational Bayescited by 3,980
  5. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  6. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 754
  7. 2019Defending Against Neural Fake Newscited by 553
  8. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 490
  9. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  10. 2019A Multiscale Visualization of Attention in the Transformer Modelcited by 292
  11. 2018Adversarial Example Generation with Syntactically Controlled Paraphrase Networkscited by 282
  12. 2017Toward Controlled Generation of Textcited by 271
23 more
  1. 2017The E2E Dataset: New Challenges For End-to-End Generationcited by 252
  2. 2017Style Transfer from Non-Parallel Text by Cross-Alignmentcited by 246
  3. 2017Challenges in Data-to-Document Generationcited by 226
  4. 2019TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agentscited by 188
  5. 2015Visualizing and Understanding Recurrent Networkscited by 158
  6. 2018Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontierscited by 117
  7. 2014Neural Variational Inference and Learning in Belief Networkscited by 116
  8. 2020Text-to-Text Pre-Training for Data-to-Text Taskscited by 104
  9. 2018Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Modelscited by 69
  10. 2018Learning Neural Templates for Text Generationcited by 61
  11. 2020CoCon: A Self-Supervised Approach for Controlled Text Generationcited by 47
  12. 2019Controllable Paraphrase Generation with a Syntactic Exemplarcited by 44
  13. 2017Understanding Hidden Memories of Recurrent Neural Networkscited by 43
  14. 2020Torch-Struct: Deep Structured Prediction Librarycited by 40
  15. 2019exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Modelscited by 33
  16. 2015What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignmentcited by 31
  17. 2016Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Stringscited by 27
  18. 2020Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondencecited by 25
  19. 2019Visual Interaction with Deep Learning Models through Collaborative Semantic Inferencecited by 24
  20. 2020Posterior Control of Blackbox Generationcited by 16
  21. 2018Controlling Personality-Based Stylistic Variation with Neural Natural Language Generatorscited by 14
  22. 2020Latent Template Induction with Gumbel-CRFscited by 9
  23. 2020MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platformcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 6; 2024: 1; 2025: 2.

11 papers.

  1. 2023Visual Analytics For Machine Learning: A Data Perspective Surveycited by 25
  2. 2023Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generationcited by 14
  3. 2022Mediators: Conversational Agents Explaining NLP Model Behaviorcited by 7
  4. 2023Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challengescited by 6
  5. 2024generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptationcited by 5
  6. 2023Visual Analytics for Efficient Image Exploration and User-Guided Image Captioningcited by 4
  7. 2025Illuminating LLM Coding Agents: Visual Analytics for Deeper Understanding and Enhancementcited by 3
  8. 2022GreaseVision: Rewriting the Rules of the Interfacecited by 2
  9. 2025Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analyticscited by 1
  10. 2023Eliciting Model Steering Interactions from Users via Data and Visual Design Probescited by 0
  11. 2023A Large Language Model-based Computational Approach to Improve Identity-Related Write-Upscited by 0