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

arXiv: https://arxiv.org/abs/2110.10185 · HTML: https://mlsanity.com/paper/2110.10185/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 63 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [A Multiscale Visualization of Attention in the Transformer Model](https://mlsanity.com/paper/1906.05714/) (2019)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [Visualizing and Understanding Recurrent Networks](https://mlsanity.com/paper/1506.02078/) (2015)
- [Neural Variational Inference and Learning in Belief Networks](https://mlsanity.com/paper/1402.0030/) (2014)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models](https://mlsanity.com/paper/1804.09299/) (2018)
- [Learning Neural Templates for Text Generation](https://mlsanity.com/paper/1808.10122/) (2018)
- [CoCon: A Self-Supervised Approach for Controlled Text Generation](https://mlsanity.com/paper/2006.03535/) (2020)
- [Understanding Hidden Memories of Recurrent Neural Networks](https://mlsanity.com/paper/1710.10777/) (2017)
- [Controllable Paraphrase Generation with a Syntactic Exemplar](https://mlsanity.com/paper/1906.00565/) (2019)
- [Torch-Struct: Deep Structured Prediction Library](https://mlsanity.com/paper/2002.00876/) (2020)
- [exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models](https://mlsanity.com/paper/1910.05276/) (2019)
- [What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment](https://mlsanity.com/paper/1509.00838/) (2015)
- [Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings](https://mlsanity.com/paper/1606.05491/) (2016)
- [Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence](https://mlsanity.com/paper/2005.01096/) (2020)

## Built on by (most cited first, 11 of 11)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation](https://mlsanity.com/paper/2310.10706/) (2023)
- [Mediators: Conversational Agents Explaining NLP Model Behavior](https://mlsanity.com/paper/2206.06029/) (2022)
- [Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges](https://mlsanity.com/paper/2310.11252/) (2023)
- [generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation](https://mlsanity.com/paper/2403.07627/) (2024)
- [Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning](https://mlsanity.com/paper/2311.01016/) (2023)
- [Illuminating LLM Coding Agents: Visual Analytics for Deeper Understanding and Enhancement](https://mlsanity.com/paper/2508.12555/) (2025)
- [GreaseVision: Rewriting the Rules of the Interface](https://mlsanity.com/paper/2204.03731/) (2022)
- [Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics](https://mlsanity.com/paper/2502.18681/) (2025)
- [Eliciting Model Steering Interactions from Users via Data and Visual Design Probes](https://mlsanity.com/paper/2310.09314/) (2023)
- [A Large Language Model-based Computational Approach to Improve Identity-Related Write-Ups](https://mlsanity.com/paper/2312.16659/) (2023)

## 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 .