Papers by Sarah Masud
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech (2024.findings-acl)
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| Challenge: | Existing language models to generate implicit hate explanations are lacking in many fields. |
| Approach: | They propose to use language models to generate explicit hate posts to make it clear . they find that simpler models incorporating external toxicity signals outperform KG-infused models . |
| Outcome: | The proposed setup produces more precise explanations than zero-shot GPT-3.5, highlighting the intricate nature of the task. |
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. |
| Outcome: | The proposed model is able to leverage community-based flagging efforts and exposure to adversaries. |
Probing Critical Learning Dynamics of PLMs for Hate Speech Detection (2024.findings-eacl)
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| Challenge: | Existing studies on pretrained language models (PLMs) for hate speech detection have not investigated how their performance is affected by pretraining and finetuning. |
| Approach: | They propose to compare pretrained language models, evaluate their seed robustness, finetuning settings, and the impact of pretraining data collection time. |
| Outcome: | The proposed models show that they are more robust than other models and that they have a better chance of performing better than domain-specific models. |
QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs (2025.coling-main)
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| Challenge: | QUENCH is a text-based English quizzing benchmarking system for large language models (LLMs). |
| Approach: | They propose a text-based English Quizzing Benchmark manually curated from YouTube quiz videos. |
| Outcome: | The proposed system assesses the world knowledge and deduction capabilities of large language models via a zero-shot, open-domain quizzing setup. |