mlsanityData to 7 Oct 2026

Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models

arXiv 1804.09299 ↗First posted 25 Apr 2018cs.CL cs.AI cs.NEcited by 69

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Abstract (arXiv)

Neural Sequence-to-Sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work in a five stage blackbox process that involves encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction with a trained sequence-to-sequence model through each stage of the translation process. The aim is to identify which patterns have been learned and to detect model errors. We demonstrate the utility of our tool through several real-world large-scale sequence-to-sequence use cases.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2018.

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Citing papers per year: 2021: 11; 2022: 12; 2023: 19; 2024: 13; 2025: 7; 2026: 7.

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  1. 2023Explainability for Large Language Models: A Surveycited by 331
  2. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 155
  3. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  4. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 126
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  8. 2022On the Explainability of Natural Language Processing Deep Modelscited by 37
  9. 2023VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformerscited by 32
  10. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  11. 2023How Does Attention Work in Vision Transformers? A Visual Analytics Attemptcited by 29
  12. 2022ferret: a Framework for Benchmarking Explainers on Transformerscited by 26
38 more
  1. 2023Visual Analytics For Machine Learning: A Data Perspective Surveycited by 25
  2. 2023RELIC: Investigating Large Language Model Responses using Self-Consistencycited by 23
  3. 2021VisQA: X-raying Vision and Language Reasoning in Transformerscited by 19
  4. 2021Relating Neural Text Degeneration to Exposure Biascited by 19
  5. 2022Symphony: Composing Interactive Interfaces for Machine Learningcited by 18
  6. 2022A Unified Understanding of Deep NLP Models for Text Classificationcited by 18
  7. 2021LMdiff: A Visual Diff Tool to Compare Language Modelscited by 17
  8. 2022Diagnosing AI Explanation Methods with Folk Concepts of Behaviorcited by 15
  9. 2022Visual Comparison of Language Model Adaptationcited by 14
  10. 2021SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarizationcited by 13
  11. 2022Diagnosing Ensemble Few-Shot Classifierscited by 13
  12. 2021GenNI: Human-AI Collaboration for Data-Backed Text Generationcited by 11
  13. 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvementscited by 11
  14. 2024Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspectivecited by 11
  15. 2025Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safetycited by 10
  16. 2023The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inferencecited by 9
  17. 2024Understanding Large Language Model Behaviors through Interactive Counterfactual Generation and Analysiscited by 8
  18. 2021AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validationcited by 7
  19. 2023Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challengescited by 7
  20. 2024KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Promptscited by 7
  21. 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
  22. 2022A Visual Analytics System for Improving Attention-based Traffic Forecasting Modelscited by 6
  23. 2023Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challengescited by 6
  24. 2024iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summariescited by 6
  25. 2024Interactive Prompt Debugging with Sequence Saliencecited by 6
  26. 2024Exploring Empty Spaces: Human-in-the-Loop Data Augmentationcited by 6
  27. 2023Visual Explanations with Attributions and Counterfactuals on Time Series Classificationcited by 5
  28. 2023Decoding Layer Saliency in Language Transformerscited by 5
  29. 2023ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinementcited by 5
  30. 2024generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptationcited by 5
  31. 2024Lightweight Safety Classification Using Pruned Language Modelscited by 5
  32. 2021FedNLP: An interpretable NLP System to Decode Federal Reserve Communicationscited by 4
  33. 2023Visual Analytics for Efficient Image Exploration and User-Guided Image Captioningcited by 4
  34. 2025Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methodscited by 4
  35. 2021Explaining the Road Not Takencited by 3
  36. 2023Interpretability in Activation Space Analysis of Transformers: A Focused Surveycited by 3
  37. 2023Semantic-Based Neural Network Repaircited by 3
  38. 2023Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Researchcited by 3

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