Prompting Across Time: Evaluating LLMs on Historical and Contemporary Offensive Language (2026.findings-acl)
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| Challenge: | Existing research on large language models and hate speech detection has focused on contemporary data. |
| Approach: | They propose to use a modular prompt design to evaluate early-modern English invectives . they propose to widen the scope of NLP research on hate speech beyond the contemporary domain . |
| Outcome: | The proposed model outperforms a modern hate-speech benchmark on Early Modern English invectives . the results show that the model is more robust to contextual and contextual factors than the current model . |
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| Challenge: | Offensive language detection systems require extensive tuning and careful model design . a resource gap exists for addressing offensive languages, especially those transcribed in non-native scripts . |
| Approach: | They evaluate pre-trained LLMs using different transcriptions of the Urdu language to assess their performance . they find that they can detect hateful and offensive content in diverse linguistic contexts . |
| Outcome: | The proposed methods perform comparable to fine-tuned benchmarks in diverse languages. |
Probing LLMs for hate speech detection: strengths and vulnerabilities (2023.findings-emnlp)
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| Challenge: | Recent efforts to detect hateful or toxic language using large language models have not used explanation, additional context and victim community information in the detection process. |
| Approach: | They use different prompt variations, input information and victim community information to evaluate large language models in zero shot setting without adding any in-context examples. |
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LLM Sensitivity Challenges in Abusive Language Detection: Instruction-Tuned vs. Human Feedback (2025.coling-main)
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| Challenge: | Existing studies show that instruction-tuned LLMs under-predict positive classes . however, they are overly sensitive and can be applied for abuse detection without fine-tuning . |
| Approach: | They show that instruction-tuned LLMs tend to under-predict positive classes . they also show that label frequency in the prompt helps with the significant over-prediction . |
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Hate Personified: Investigating the role of LLMs in content moderation (2024.emnlp-main)
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| Challenge: | Our work provides preliminary guidelines and highlights the nuances of applying Large Language models in culturally sensitive cases. |
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Distilling the Essence, Discarding the Dross: Improving Fairness in Multimodal Large Language Models via Historical Reflection-Guided Prompt Optimization (2026.findings-acl)
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| Challenge: | Existing approaches to debiase MLLMs rely on handcrafted prompts that are brittle and difficult to generalize across tasks and bias types. |
| Approach: | They propose an adaptive self-debiasing framework that optimizes task-specific debiasers to suppress stereotypical outputs. |
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Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech (2021.findings-emnlp)
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| Challenge: | Existing methods for hate speech detection are limited in size and lack of labeled datasets. |
| Approach: | They employ pretrained language models to generate large amounts of hate speech sequences from available labeled examples. |
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PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are useful for low-resource scenarios and time-restricted applications. |
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Fine-Tuned LLMs are “Time Capsules” for Tracking Societal Bias Through Books (2025.naacl-long)
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| Challenge: | We develop a corpus comprising 593 fictional books across seven decades (1950-2019) to track bias evolution. |
| Approach: | They develop a method to trace and quantify bias evolution using fine-tuned LLMs on fictional books across seven decades to track bias evolution. |
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Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models (2024.findings-emnlp)
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| Challenge: | HS is any communication demeaning a person or a group based on social or ethnic characteristics that undermines social harmony and individual safety . the recent Israel-Hamas conflict has escalated both anti-Muslim and anti-Semitic sentiments worldwide . |
| Approach: | They examine the role of large language models and large multimodal models in HS moderation . they examine how text, images, and audio interact to spread hate speech . |
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Don’t Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection (2024.acl-long)
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| Challenge: | Several studies have examined whether large language models exhibit bias or discrimination against individuals or groups in terms of protected attributes like race, gender, or religion. |
| Approach: | They evaluate LLMs' ability to detect implicit hate speech and express confidence in their responses by considering prompt patterns and mainstream uncertainty estimation methods. |
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