LLMs to Replace Crowdsourcing For Parallel Data Creation? The Case of Text Detoxification (2024.findings-emnlp)
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| Challenge: | Manual annotation methods, such as crowdsourcing, are costly and require intricate task design skills. |
| Approach: | They propose to use open source LLMs to annotate parallel data for text detoxification . they generate a pseudo-parallel detoxification dataset using activation patching . |
| Outcome: | The proposed model performs comparable to the original dataset in automatic detoxification evaluation metrics and superior quality in manual evaluation and side-by-side comparisons. |
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| Challenge: | Existing approaches to multilingual text detoxification are hampered by the scarcity of parallel multilingual datasets. |
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