Summary of Faithfulness and the Notion Of Adversarial Sensitivity in Nlp Explanations, by Supriya Manna and Niladri Sett
Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations
by Supriya Manna, Niladri Sett
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Adversarial Sensitivity approach provides a novel way to evaluate the faithfulness of explainable AI models in NLP. Current methods are often biased and fail to capture the true reasoning behind model decisions. The Adversarial Sensitivity method accounts for the faithfulness of explainer responses by analyzing how they change under adversarial input perturbations. This work addresses significant limitations in existing evaluation techniques and provides a more comprehensive understanding of faithfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to check if AI models are reliable and honest about their decisions. Right now, methods for checking this reliability are often flawed and don’t accurately capture how the model thinks. The new approach looks at how well an explanation changes when the input is changed in tricky ways. This helps fix some big problems with current evaluation methods and gives us a better understanding of what makes AI explanations reliable. |
Keywords
» Artificial intelligence » Nlp
