Summary of Discern: Decoding Systematic Errors in Natural Language For Text Classifiers, by Rakesh R. Menon et al.
DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers
by Rakesh R. Menon, Shashank Srivastava
First submitted to arxiv on: 29 Oct 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 The proposed framework, DISCERN, aims to address systematic biases in machine learning systems by generating precise natural language descriptions of these errors. The method iteratively generates explanations between two large language models, which are then used to improve classifier training sets through active learning. Experimental results show consistent performance improvements on three text-classification datasets, demonstrating the effectiveness of DISCERN in mitigating biases. Furthermore, human evaluations indicate that users can better understand and interpret systematic biases when described through language explanations rather than cluster exemplars. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning systems are getting really good at predicting things, but they often make mistakes because of how they were trained. Sometimes these mistakes are biased against certain groups or ideas. A new way to fix this is by explaining why the system made a mistake. The researchers created a tool called DISCERN that does just that. It takes two big language models and makes them work together to explain the biases. Then, it uses those explanations to make the system better. In tests, DISCERN worked really well and people could understand the mistakes much better when they were explained in simple terms. |
Keywords
» Artificial intelligence » Active learning » Machine learning » Text classification
