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2101.00288
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Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
1 January 2021
Tongshuang Wu
Marco Tulio Ribeiro
Jeffrey Heer
Daniel S. Weld
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Papers citing
"Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models"
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Title
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Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification
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Zhijin Wu
Wei Yu Wu
Chenyang Yang
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FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
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01 Jan 2025
The Evolution of LLM Adoption in Industry Data Curation Practices
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Emily Reif
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Minsuk Kahng
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Gumbel Counterfactual Generation From Language Models
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A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers
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113
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21
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32
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19
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A Survey on Natural Language Counterfactual Generation
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Xiaoqi Qiu
Yu Yue
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34
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Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
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Rachel Rudinger
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4
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Aradhana Sinha
Yao Qin
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46
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Beyond Agreement: Diagnosing the Rationale Alignment of Automated Essay Scoring Methods based on Linguistically-informed Counterfactuals
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Renfen Hu
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32
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0
29 May 2024
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35
1
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43
1
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Data Science Principles for Interpretable and Explainable AI
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0
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Mitigating Text Toxicity with Counterfactual Generation
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Nina Achache
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2
0
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Zero-shot LLM-guided Counterfactual Generation for Text
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38
4
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CEval: A Benchmark for Evaluating Counterfactual Text Generation
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29
5
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LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study
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37
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Does It Make Sense to Explain a Black Box With Another Black Box?
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1
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A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation
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