# Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models

arXiv: https://arxiv.org/abs/1905.09864 · HTML: https://mlsanity.com/paper/1905.09864/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 8 of 8)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Putting Humans in the Natural Language Processing Loop: A Survey](https://mlsanity.com/paper/2103.04044/) (2021)
- [Think Together and Work Better: Combining Humans' and LLMs' Think-Aloud Outcomes for Effective Text Evaluation](https://mlsanity.com/paper/2409.07355/) (2024)
- [A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System](https://mlsanity.com/paper/2304.01774/) (2023)
- [How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond](https://mlsanity.com/paper/2501.05714/) (2025)
- [User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics](https://mlsanity.com/paper/2101.02244/) (2021)
- [Topic-time Heatmaps for Human-in-the-loop Topic Detection and Tracking](https://mlsanity.com/paper/2110.07337/) (2021)
- [A Visual Tool for Interactive Model Explanation using Sensitivity Analysis](https://mlsanity.com/paper/2508.04269/) (2025)

## Abstract (arXiv)

To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments. These approaches extend previously proposed frameworks, including constraints and informed prior-based methods. Users should have a sense of control in HLTM systems, so we propose a control metric to measure whether refinement operations' results match users' expectations. Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics.