Papers with Toxicity detection
Implanting LLM’s Knowledge via Reading Comprehension Tree for Toxicity Detection (2024.findings-acl)
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| Challenge: | Existing methods for toxic content detection are small language model (SLM) based and large language model(LLM) -based. |
| Approach: | They propose to implant LLM's knowledge into SLM based methods to stick to both types of models' strengths by constructing a reading comprehension tree to transfer knowledge between two models. |
| Outcome: | The proposed method can stick to both types of models' strengths . it is compared with existing methods on real-world and machine-generated datasets. |
Performance and Risk Trade-offs for Multi-word Text Prediction at Scale (2023.findings-eacl)
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Aniket Vashishtha, S Sai Prasad, Payal Bajaj, Vishrav Chaudhary, Kate Cook, Sandipan Dandapat, Sunayana Sitaram, Monojit Choudhury
| Challenge: | Large Language Models (LLMs) generate ethically inappropriate texts even for seemingly innocuous contexts. |
| Approach: | They propose to use large language models to detect and filter toxic content in text prediction tasks by evaluating their toxicity detection approaches against a manually crafted CheckList of harms. |
| Outcome: | The proposed methods are compared against a checklist of harms targeted at different groups and different levels of severity in English. |
Speak No Evil, Just Prompt: Low-resource Multilingual Toxic Speech Detection with Audio Language Model (2026.findings-acl)
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| Challenge: | Existing methods for toxic speech detection rely on high-resource languages and lack acoustic cues. |
| Approach: | They propose a prompt-based adaptation framework that performs end-to-end toxicity detection without ASR. |
| Outcome: | The proposed framework achieves a micro-averaged ROC-AUC of 98.07% on polySpeechTox . it is based on a frozen audio language model and can perform end-to-end toxicity detection without ASR . |
ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos (2024.findings-acl)
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| Challenge: | Using a dataset of 931 videos with 4021 code-mixed Hindi-English utterances, we find that video content with multiple modalities is more accurate and more accurate than textual content. |
| Approach: | They propose to use a dataset to analyze toxic content in video content in non-English languages by leveraging language models. |
| Outcome: | The proposed framework achieves an Accuracy and Weighted F1 score of 94.29% and 94.35% for the first time in its class. |