Papers by Lingzi Hong
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. |
A Fine-Grained Taxonomy of Replies to Hate Speech (2023.emnlp-main)
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| Challenge: | a new corpus of responses to hate speech is developed to counter hate speech . authors work with real, user-generated hate speech and all the replies it elicits . counterspeech refers to a "direct response that counters hate speech" |
| Approach: | They propose a taxonomy of responses to hate speech and a new corpus to analyze responses . they find that responses to user-generated hate speech are more effective than replies generated by a third party . |
| Outcome: | The proposed taxonomy of responses to hate speech and a new corpus provide insights into content real users reply with and which replies are empirically most effective. |
Echoes of Discord: Forecasting Hater Reactions to Counterspeech (2025.findings-naacl)
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| Challenge: | Hate speech (HS) online causes increased prejudice and discrimination, fostering an environment of hostility and social division. |
| Approach: | They analyze the Reddit Echoes of Hate dataset to assess the impact of counterspeech from the hater's perspective and focus on whether the counterspeak leads the reentry to be hateful. |
| Outcome: | The proposed model outperforms the two-stage reaction predictor and the three-way classifier to predict haters' reactions to the reentry of the conversation and determines the type of resentment. |
A Dynamic Fusion Model for Consistent Crisis Response (2025.findings-emnlp)
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| Challenge: | a critical yet often overlooked factor is the consistency of response style . few studies have explored methods for maintaining stylistic consistency across generated responses . |
| Approach: | They propose a metric for evaluating style consistency and introduce a method for fusion-based generation . |
| Outcome: | The proposed method outperforms baselines in response quality and stylistic uniformity. |
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. |
| Approach: | They develop automatic counterspeech generation methods that incorporate two desired conversation outcomes into the text generation process: low conversation incivility and non-hateful hater reentry. |
| Outcome: | The proposed methods incorporate two desired conversation outcomes: low conversation incivility and non-hateful hater reentry. |
Hate Speech and Counter Speech Detection: Conversational Context Does Matter (2022.naacl-main)
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| Challenge: | Existing datasets and models target hate speech but ignore context . Existing models target either hate speech or hate and counter speech but disregard context - a new study shows that context is critical to identify hate and anti-hate speech. |
| Approach: | They propose to use context to identify hate and counter speech in a reddit conversation thread. |
| Outcome: | The proposed model improves when and why context is taken into account. |
Assessing the Human Likeness of AI-Generated Counterspeech (2025.coling-main)
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| Challenge: | Existing studies have focused on relevance, surface form, and other shallow linguistic characteristics. |
| Approach: | They propose to evaluate the human likeness of AI-generated counterspeech . they implement and evaluate several LLM-based generation strategies . |
| Outcome: | The proposed models show that human-written counterspeech can be distinguished by both simple classifiers and humans. |