Mitigating Shortcuts in Language Models with Soft Label Encoding (2024.lrec-main)
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| Challenge: | Recent studies have shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. |
| Approach: | They propose a framework for debiasing shortcuts and a dummy class to encode shortcuts into a model and use it to generate soft labels. |
| Outcome: | The proposed framework significantly improves out-of-distribution generalization while maintaining satisfactory in-district accuracy. |
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| Challenge: | Existing work identifies task-specific shortcuts via human priors or error analyses, which requires extensive expertise and efforts. |
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| Challenge: | Recent large language models (LLMs) have incredible instruction-following capabilities while maintaining strong task completion ability. |
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| Challenge: | Debiasing language models from unwanted behaviors in natural language understanding datasets is a topic with increasing interest in the NLP community. |
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Navigating the Shortcut Maze: A Comprehensive Analysis of Shortcut Learning in Text Classification by Language Models (2024.findings-emnlp)
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| Challenge: | Language models (LMs) often rely on spurious correlations rather than causally relevant features to improve accuracy and generalizability. |
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Stubborn Lexical Bias in Data and Models (2023.findings-acl)
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| Challenge: | Recent work has focused on spurious correlations between features and labels in training data . but, we find strong evidence of corresponding bias in the trained models . |
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| Challenge: | Existing methods to debiase samples with biased features obstructs the model in learning from non-biased parts of the samples. |
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| Challenge: | Experimental results show that our method consistently outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets, while maintaining high in-distance accuracy. |
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