Summary of Sidu-txt: An Xai Algorithm For Nlp with a Holistic Assessment Approach, by Mohammad N.s. Jahromi et al.
SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach
by Mohammad N.S. Jahromi, Satya. M. Muddamsetty, Asta Sofie Stage Jarlner, Anna Murphy Høgenhaug, Thomas Gammeltoft-Hansen, Thomas B. Moeslund
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper explores the applicability of Explainable AI (XAI) methods for text-based data. The authors extend a previously recognized method, Similarity Difference and Uniqueness (SIDU), to textual data, creating SIDU-TXT. This extended method generates heatmaps at a granular level, providing explanations that highlight contextually significant textual elements crucial for model predictions. To evaluate the effectiveness of SIDU-TXT, the study employs a comprehensive three-tiered evaluation framework: Functionally-Grounded, Human-Grounded, and Application-Grounded. In sentiment analysis tasks on movie reviews, SIDU-TXT excels in both functionally and human-grounded evaluations, outperforming benchmarks like Grad-CAM and LIME. However, in the legal domain of asylum decision-making, both SIDU-TXT and Grad-CAM fall short of meeting expert expectations, highlighting the need for further research in XAI methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make artificial intelligence more understandable by explaining its decisions. The authors take a method that works well with images and adapt it for text data. They create a new method called SIDU-TXT, which shows where important words are in the text that the AI uses to make predictions. To see if this method is good, they use three ways to test it: looking at how well it does a specific job, asking humans to rate its explanations, and seeing if it works well in real-life situations. In one case, the method did very well and was better than other methods. However, when applied to a difficult and sensitive topic like asylum decision-making, the method didn’t quite meet the high standards set by experts. |
