# The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inference

arXiv: https://arxiv.org/abs/2301.04528 · HTML: https://mlsanity.com/paper/2301.04528/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 52 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [A Multiscale Visualization of Attention in the Transformer Model](https://mlsanity.com/paper/1906.05714/) (2019)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Analysis Methods in Neural Language Processing: A Survey](https://mlsanity.com/paper/1812.08951/) (2018)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [What do Neural Machine Translation Models Learn about Morphology?](https://mlsanity.com/paper/1704.03471/) (2017)
- [The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models](https://mlsanity.com/paper/2008.05122/) (2020)
- [Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models](https://mlsanity.com/paper/1804.09299/) (2018)
- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models](https://mlsanity.com/paper/2009.07053/) (2020)
- [Controlling Hallucinations at Word Level in Data-to-Text Generation](https://mlsanity.com/paper/2102.02810/) (2021)
- [The Low-Dimensional Linear Geometry of Contextualized Word Representations](https://mlsanity.com/paper/2105.07109/) (2021)
- [Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples](https://mlsanity.com/paper/1912.04853/) (2019)
- [NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networks](https://mlsanity.com/paper/1812.09359/) (2018)

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

- [LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models](https://mlsanity.com/paper/2402.10524/) (2024)
- [Automatic Histograms: Leveraging Language Models for Text Dataset Exploration](https://mlsanity.com/paper/2402.14880/) (2024)
- [Understanding Large Language Model Behaviors through Interactive Counterfactual Generation and Analysis](https://mlsanity.com/paper/2405.00708/) (2024)
- [Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference](https://mlsanity.com/paper/2404.03085/) (2024)
- [Exploring Empty Spaces: Human-in-the-Loop Data Augmentation](https://mlsanity.com/paper/2410.01088/) (2024)
- [The Evolution of LLM Adoption in Industry Data Curation Practices](https://mlsanity.com/paper/2412.16089/) (2024)
- [Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models](https://mlsanity.com/paper/2305.11364/) (2023)
- [RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems](https://mlsanity.com/paper/2601.12991/) (2026)
- [A Visual Tool for Interactive Model Explanation using Sensitivity Analysis](https://mlsanity.com/paper/2508.04269/) (2025)

## Abstract (arXiv)

With a constant increase of learned parameters, modern neural language models become increasingly more powerful. Yet, explaining these complex model's behavior remains a widely unsolved problem. In this paper, we discuss the role interactive visualization can play in explaining NLP models (XNLP). We motivate the use of visualization in relation to target users and common NLP pipelines. We also present several use cases to provide concrete examples on XNLP with visualization. Finally, we point out an extensive list of research opportunities in this field.