Summary of A Comparative Analysis Of Counterfactual Explanation Methods For Text Classifiers, by Stephen Mcaleese and Mark Keane
A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers
by Stephen McAleese, Mark Keane
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel evaluation of five counterfactual explanation methods for a BERT-based text classifier on two datasets reveals that white-box substitution-based approaches are effective at generating valid explanations, while large language model (LLM)-based techniques excel in producing natural yet often invalid counterfactuals. The study recommends developing hybrid methods combining the strengths of established and newer techniques to generate high-quality explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers tested different ways to explain why a text classifier makes certain predictions. They found that some old methods work well, while new approaches using large language models are good at making the explanations sound natural, but not always accurate. The scientists suggest combining these two types of methods to create even better explanations. |
Keywords
» Artificial intelligence » Bert » Large language model
