Papers with Content moderation
STAND-Guard: A Small Task-Adaptive Content Moderation Model (2025.coling-industry)
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| Challenge: | Content moderation is important for developing welcoming online platforms and responsible large language models. |
| Approach: | They propose a small task-adaptive coNtent moDeration model that can be easily adapted to new or customized content moderation tasks without extensive model tuning. |
| Outcome: | The proposed model is comparable to GPT-3.5-Turbo on unseen English binary classification tasks. |
Representing and Clustering Errors in Offensive Language Detection (2025.naacl-srw)
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| Challenge: | Sentence-BERT embeddings of Large Language Model (LLM)-generated linguistic features give the most interpretable clustering for Arabic errors. |
| Approach: | They evaluate the K-Means clustering of four text representations for the task of offensive language detection in English and Levantine Arabic. |
| Outcome: | The proposed clustering of four text representations for offensive language detection in English and Levantine Arabic gives the most human-interpretable clustering for English errors and the grouping is mainly based on the targeted group in the text. |
Content Moderation for Evolving Policies using Binary Question Answering (2023.acl-industry)
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| Challenge: | Social media platforms use content moderation to safeguard users from abuse, harassment, malicious attacks, spam, etc. |
| Approach: | They propose to model content moderation as a binary question answering problem where questions validate loosely coupled themes constituting a policy. |
| Outcome: | The proposed model improves recall at 95% precision on two proprietary datasets of social media posts and comments respectively annotated under curated Hate Speech and Commercial Spam policies. |
Multilingual Content Moderation: A Case Study on Reddit (2023.eacl-main)
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| Challenge: | a growing need for AI moderators to safeguard users and protect mental health of human moderator from traumatic content. |
| Approach: | They propose to use a multilingual dataset to study the challenges of content moderation . they propose to analyze 1.8 million Reddit comments in English, german, spanish and french . |
| Outcome: | The proposed dataset highlights the challenges and suggests related research problems . it shows that the proposed model can be used to predict the violated rule . |
CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have enabled more sophisticated content moderation, but these methods lack generalization, interpretability, and adaptability to unseen or ambiguous cases. |
| Approach: | They propose a new moderation framework that leverages analogical examples to enhance rule induction and decision reliability. |
| Outcome: | The proposed method outperforms rule-injected fine-tuning baselines and multi-stage static RAG pipelines in terms of moderation accuracy and rule quality. |