Summary of “show Me What’s Wrong!”: Combining Charts and Text to Guide Data Analysis, by Beatriz Feliciano et al.
“Show Me What’s Wrong!”: Combining Charts and Text to Guide Data Analysis
by Beatriz Feliciano, Rita Costa, Jean Alves, Javier Liébana, Diogo Duarte, Pedro Bizarro
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a tool to facilitate anomaly detection in high-dimensional financial transactional data for fraud detection purposes. The tool combines automated highlights, Large Language Model-generated insights, and visual analytics to enable efficient exploration at various levels of detail. By segmenting the data into analysis areas and using visual cues, the system signals which segments require closer examination. Upon user selection, the tool provides textual and graphical summaries, allowing for quick understanding of relevant details. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The tool helps analysts quickly identify suspicious activity by providing a high-level overview of the data and enabling detailed exploration through graphical representations. The tool’s effectiveness was tested with seven domain experts who found it supported and guided their exploratory analysis, making it easier to identify suspicious information. |
Keywords
* Artificial intelligence * Anomaly detection * Large language model
