GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection? (2024.lrec-main)
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| Challenge: | HateCheck test cases are generic and have simplistic sentence structures that do not match the real-world data. |
| Approach: | They propose a framework to generate more diverse and realistic functional tests from scratch by instructing large language models. |
| Outcome: | The proposed framework generates more diverse and realistic functional tests from scratch by instructing large language models (LLMs). |
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| Challenge: | Hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. |
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| Challenge: | Existing methods for hate speech detection are limited in size and lack of labeled datasets. |
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Evaluating ChatGPT against Functionality Tests for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Hate speech detection models are evaluated on a held-out test data, but they are incapable of identifying weaknesses. |
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| Challenge: | Recent work on synthetic data for training models for NLP tasks reports mixed results on subjective tasks such as hate speech detection. |
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Zsolt T. Kardkovács, Lynda Djennane, Anna Field, Boualem Benatallah, Yacine Gaci, Fabio Casati, Walid Gaaloul
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| Challenge: | Existing fact-checking approaches require access to external databases or external databases . a lack of external databases can undermine trust in large language models. |
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Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages (2022.emnlp-main)
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| Challenge: | Hate speech datasets focus on English-language content, hindering effective models . annotating hateful content is expensive, time-consuming and potentially harmful to annotators. |
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