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

arXiv: https://arxiv.org/abs/1804.09299 · HTML: https://mlsanity.com/paper/1804.09299/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 30 of 69)

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- [Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective](https://mlsanity.com/paper/2402.00262/) (2024)
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- [The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inference](https://mlsanity.com/paper/2301.04528/) (2023)
- [Understanding Large Language Model Behaviors through Interactive Counterfactual Generation and Analysis](https://mlsanity.com/paper/2405.00708/) (2024)
- [AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation](https://mlsanity.com/paper/2109.05629/) (2021)

## 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.