Papers with online harassment

3 papers
Giving Control Back to Models: Enabling Offensive Language Detection Models to Autonomously Identify and Mitigate Biases (2024.findings-emnlp)

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Challenge: Existing models often rely on specific words to predict offensive content, compromising model fairness and potentially exacerbates biases against vulnerable and minority groups.
Approach: They propose a bias self-awareness and data self-iteration framework to help models identify and mitigate biases by integrating multiple natural language processing techniques.
Outcome: The proposed framework reduces false positive rate of models in in-distribution and out-of-difference tests, enhances model accuracy and fairness, and shows promising performance improvements on larger datasets.
Word-Level Detection of Code-Mixed Hate Speech with Multilingual Domain Transfer (2025.findings-acl)

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Challenge: a growing problem in language detection tasks is code-mixing, a combination of more than one language . lack of available datasets for code-mixing causes the problem . authors propose a multilingual approach to code-matching .
Approach: They propose to use an annotated hate speech dataset to detect code-mixing in profane language . they propose to apply bilingual fine-tuned models to code-mixed hate speech in german rap lyrics .
Outcome: The proposed model can detect code-mixed hate speech and neologisms in German rap lyrics . the proposed model is more nuanced than binary classification .
Adaptively profiling models with task elicitation (2025.emnlp-main)

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Challenge: Language model evaluations fail to characterize consequential failure modes, forcing experts to inspect outputs and build new benchmarks.
Approach: They propose a method that automatically builds new evaluations to profile model behavior.
Outcome: The proposed method finds that language models fail in hundreds of tasks . it also finds that o3-mini is prone to hallucination when fabrications are repeated .

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