Who Speaks Matters: Analysing the Influence of the Speaker’s Linguistic Identity on Hate Classification (2025.findings-emnlp)
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| Challenge: | Large Language Models are known to be brittle and biased against marginalised communities and dialects. |
| Approach: | They investigate the robustness of hate speech classification using LLMs when explicit and implicit markers of the speaker’s ethnicity are injected into the input. |
| Outcome: | The proposed model is robust when explicit and implicit markers of speaker's ethnicity are injected into the input. |
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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. |
| Outcome: | The proposed models perform significantly better when included in the pipeline than baseline models. |
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. |
| Approach: | They propose to use large language models to help with content moderation to assess how well the needs of diverse groups are reflected in annotated posts. |
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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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The Risk of Racial Bias in Hate Speech Detection (P19-1)
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| Challenge: | Annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. |
| Approach: | They propose *dialect* and *race priming* as ways to reduce the racial bias in hate speech detection models by detecting differences in dialects in annotated tweets. |
| Outcome: | The proposed models acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. |
Model-Dependent Moderation: Inconsistencies in Hate Speech Detection Across LLM-based Systems (2025.findings-acl)
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| Challenge: | Content moderation systems powered by large language models are increasingly deployed to detect hate speech . if two systems produce different outcomes for the same content, it undermines consistency and predictability . |
| Approach: | They analyze 1.3+ million sentences from a factorial design to determine hate speech classification . they find identical content receives markedly different classification values across systems . |
| Outcome: | The proposed model finds that identical content receives markedly different classification values across systems. |
Whose Emotions and Moral Sentiments do Language Models Reflect? (2024.findings-acl)
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| Challenge: | Existing research has focused on positional alignment, which measures how closely the models mimic the opinions and stances of different social groups. |
| Approach: | They define the problem of affective alignment, which measures how LMs’ emotional and moral tone represents those of different groups. |
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Confident, Calibrated, or Complicit: Safety Alignment and Ideological Bias in LLM Hate Speech Detection (2026.acl-long)
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| Challenge: | censored models outperform uncensoreed counterparts in accuracy and robustness, achieving 69.0% accuracy versus 64.1% strict accuracy. |
| Approach: | They examine how large language models with minimal safety alignment compare with more heavily aligned counterparts when deployed using political personas. |
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Decoding Hate: Exploring Language Models’ Reactions to Hate Speech (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) are trained on vast amounts of unmoderated internet data, enabling them to generate text autonomously. |
| Approach: | They investigate the responses of seven state-of-the-art Large Language Models (LLMs) to hate speech by qualitative analysis. |
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Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
| Approach: | They investigate the presence and nature of bias within large language models and its consequential impact on media bias detection. |
| Outcome: | The proposed debiasing strategies include prompt engineering and model fine-tuning. |
LLM generated responses to mitigate the impact of hate speech (2024.findings-emnlp)
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Jakub Podolak, Szymon Łukasik, Paweł Balawender, Jan Ossowski, Jan Piotrowski, Katarzyna Bakowicz, Piotr Sankowski
| Challenge: | a study aims to determine the effectiveness of large language models to counteract hate speech . it is the first real-life A/B test evaluating the effectiveness . |
| Approach: | They conduct the first real-life A/B test assessing the effectiveness of LLM-generated counter-speech. |
| Outcome: | The proposed system reduces user engagement by over 20%, the study shows . the proposed metric is based on a simple metric and is scalable to other platforms . |