{
 "id": "Robert-Morabito/STOP",
 "owner": "Robert-Morabito",
 "name": "STOP",
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 "last_modified": "2025-02-10T19:34:57.000Z",
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 "tasks": [
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 ],
 "citation": "@inproceedings{morabito-etal-2024-stop,\n    title = \"{STOP}! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions\",\n    author = \"Morabito, Robert  and\n      Madhusudan, Sangmitra  and\n      McDonald, Tyler  and\n      Emami, Ali\",\n    editor = \"Al-Onaizan, Yaser  and\n      Bansal, Mohit  and\n      Chen, Yun-Nung\",\n    booktitle = \"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2024\",\n    address = \"Miami, Florida, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.emnlp-main.243/\",\n    doi = \"10.18653/v1/2024.emnlp-main.243\",\n    pages = \"4221--4243\",\n    abstract = \"Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing. However, many current methodologies evaluate scenarios in isolation, without considering the broader context or the spectrum of potential biases within each situation. To address this, we introduce the Sensitivity Testing on Offensive Progressions (STOP) dataset, which includes 450 offensive progressions containing 2,700 unique sentences of varying severity that progressively escalate from less to more explicitly offensive. Covering a broad spectrum of 9 demographics and 46 sub-demographics, STOP ensures inclusivity and comprehensive coverage. We evaluate several leading closed- and open-source models, including ",
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