Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL (2025.findings-emnlp)
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| Challenge: | Existing approaches produce uniform responses, ignoring that health literacy levels affect the accessibility and effectiveness of counterspeech. |
| Approach: | They propose a Controlled-Literacy framework that generates counterspeech adapted to different health literacy levels. |
| Outcome: | The proposed framework outperforms baselines by generating more accessible counterspeech to health misinformation. |
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| Challenge: | Existing systems that target hate speech with intent-conditioned counterspeech generate better results with longer contexts. |
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| Challenge: | Social media use is growing annually with about 68.5% of the global population active on these platforms as of July 2025. |
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| Challenge: | Existing approaches to attack large language models rely heavily on retrieval and generation stages, limiting their effectiveness in black-box scenarios. |
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Rationale-Guided Retrieval Augmented Generation for Medical Question Answering (2025.naacl-long)
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Jiwoong Sohn, Yein Park, Chanwoong Yoon, Sihyeon Park, Hyeon Hwang, Mujeen Sung, Hyunjae Kim, Jaewoo Kang
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| Challenge: | tutorial aims to show how counterspeech is used to tackle abuse and misinformation by individuals, activists and organisations. |
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Outcome-Constrained Large Language Models for Countering Hate Speech (2024.emnlp-main)
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| Challenge: | Existing research focuses on generating counterspeech with linguistic attributes such as being polite, informative, and intent-driven. |
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