User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text
Analytics
- URL: http://arxiv.org/abs/2101.02244v1
- Date: Wed, 6 Jan 2021 19:44:11 GMT
- Title: User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text
Analytics
- Authors: Anamaria Crisan, Michael Correll
- Abstract summary: We conduct a simulation-based analysis of human-centered interactions with topic models.
We find that user interactions have impacts that differ in magnitude but often negatively affect the quality of the resulting modelling.
- Score: 29.552736183006672
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Topic models are widely used analysis techniques for clustering documents and
surfacing thematic elements of text corpora. These models remain challenging to
optimize and often require a "human-in-the-loop" approach where domain experts
use their knowledge to steer and adjust. However, the fragility,
incompleteness, and opacity of these models means even minor changes could
induce large and potentially undesirable changes in resulting model. In this
paper we conduct a simulation-based analysis of human-centered interactions
with topic models, with the objective of measuring the sensitivity of topic
models to common classes of user actions. We find that user interactions have
impacts that differ in magnitude but often negatively affect the quality of the
resulting modelling in a way that can be difficult for the user to evaluate. We
suggest the incorporation of sensitivity and "multiverse" analyses to topic
model interfaces to surface and overcome these deficiencies.
Related papers
- Interactive Visual Assessment for Text-to-Image Generation Models [28.526897072724662]
We propose DyEval, a dynamic interactive visual assessment framework for generative models.
DyEval features an intuitive visual interface that enables users to interactively explore and analyze model behaviors.
Our framework provides valuable insights for improving generative models and has broad implications for advancing the reliability and capabilities of visual generation systems.
arXiv Detail & Related papers (2024-11-23T10:06:18Z) - Improving the TENOR of Labeling: Re-evaluating Topic Models for Content
Analysis [5.757610495733924]
We conduct the first evaluation of neural, supervised and classical topic models in an interactive task based setting.
We show that current automated metrics do not provide a complete picture of topic modeling capabilities.
arXiv Detail & Related papers (2024-01-29T17:54:04Z) - A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System [32.065158970382036]
Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively.
Recent research has demonstrated the value of user feedback, but there are still issues to consider.
We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface.
arXiv Detail & Related papers (2023-04-04T13:05:10Z) - Are Neural Topic Models Broken? [81.15470302729638]
We study the relationship between automated and human evaluation of topic models.
We find that neural topic models fare worse in both respects compared to an established classical method.
arXiv Detail & Related papers (2022-10-28T14:38:50Z) - Temporal Relevance Analysis for Video Action Models [70.39411261685963]
We first propose a new approach to quantify the temporal relationships between frames captured by CNN-based action models.
We then conduct comprehensive experiments and in-depth analysis to provide a better understanding of how temporal modeling is affected.
arXiv Detail & Related papers (2022-04-25T19:06:48Z) - Is Automated Topic Model Evaluation Broken?: The Incoherence of
Coherence [62.826466543958624]
We look at the standardization gap and the validation gap in topic model evaluation.
Recent models relying on neural components surpass classical topic models according to these metrics.
We use automatic coherence along with the two most widely accepted human judgment tasks, namely, topic rating and word intrusion.
arXiv Detail & Related papers (2021-07-05T17:58:52Z) - Explainable Adversarial Attacks in Deep Neural Networks Using Activation
Profiles [69.9674326582747]
This paper presents a visual framework to investigate neural network models subjected to adversarial examples.
We show how observing these elements can quickly pinpoint exploited areas in a model.
arXiv Detail & Related papers (2021-03-18T13:04:21Z) - Evaluating the Interpretability of Generative Models by Interactive
Reconstruction [30.441247705313575]
We introduce a task to quantify the human-interpretability of generative model representations.
We find performance on this task much more reliably differentiates entangled and disentangled models than baseline approaches.
arXiv Detail & Related papers (2021-02-02T02:38:14Z) - Firearm Detection via Convolutional Neural Networks: Comparing a
Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents.
One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis.
We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
arXiv Detail & Related papers (2020-12-17T15:19:29Z) - On the Transferability of Adversarial Attacksagainst Neural Text
Classifier [121.6758865857686]
We investigate the transferability of adversarial examples for text classification models.
We propose a genetic algorithm to find an ensemble of models that can induce adversarial examples to fool almost all existing models.
We derive word replacement rules that can be used for model diagnostics from these adversarial examples.
arXiv Detail & Related papers (2020-11-17T10:45:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.