Beyond Generation: Leveraging LLM Creativity to Overcome Label Bias in Classification (2025.findings-acl)
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| Challenge: | Existing methods to mitigate label bias by leveraging in-domain data are often unavailable in real-world scenarios. |
| Approach: | They propose a calibration method that generates synthetic in-domain data from a few in-context demonstrations and utilizes it for calibration. |
| Outcome: | The proposed method reduces label bias by leveraging in-domain data from demonstrations. |
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| Challenge: | Existing methods to categorize label biases in in-context learning (ICL) have not addressed all three types of label bias. |
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Generative Calibration for In-context Learning (2023.findings-emnlp)
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Nihar Ranjan Sahoo, Ashita Saxena, Kishan Maharaj, Arif A. Ahmad, Abhijit Mishra, Pushpak Bhattacharyya
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. |
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