Papers by Veronica Perez-Rosas
Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions (2026.eacl-long)
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| Challenge: | Existing challenges in misinformation exposure and susceptibility vary across demographics. |
| Approach: | They propose a framework that investigates the bidirectional persuasion dynamics between LLMs and humans when exposed to misinformation. |
| Outcome: | The proposed framework analyzes the spread of misinformation under persuasion among demographic-oriented LLM agents. |
Knowledge Enhanced Reflection Generation for Counseling Dialogues (2022.acl-long)
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| Challenge: | Using retrieval and generative methods, we generate responses using commonsense and domain knowledge. |
| Approach: | They propose a pipeline that collects domain knowledge through web mining and a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. |
| Outcome: | The proposed pipeline collects domain knowledge through web mining and incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. |
VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing (2023.findings-emnlp)
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| Challenge: | During the Covid-19 pandemic, the number of people living with anxiety and depression rose more than four times . counselor training is difficult to speed up due to several factors, such as the need for expert supervision and the laborious and time-extensive process needed to provide evaluative feedback. |
| Approach: | They propose a template-based rewriting system that transforms non-reflective statements into reflective responses using paraphrase-augmented training and adaptive template updating. |
| Outcome: | The proposed model transforms non-reflective statements into more reflective responses while achieving a good content preservation-reflection style trade-off. |
Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation (2024.lrec-main)
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| Challenge: | In this paper, we analyze the problem of multi-reward reinforcement learning to optimize for multiple text qualities for natural language generation. |
| Approach: | They propose to use multi-reward reinforcement learning to optimize for multiple text qualities for natural language generation by using bandits. |
| Outcome: | The proposed techniques outperform existing naive and bandit baselines, showcasing their potential for enhancing language models. |
Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanish (2025.naacl-short)
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| Challenge: | Cultural and language factors influence counseling, but research has not explored whether this applies to other languages. |
| Approach: | They introduce a Spanish-language counseling dataset that contains expert annotations for counseling reflections and questions. |
| Outcome: | The proposed dataset explores language-based differences in counselor behavior in English and Spanish and develops classifiers in monolingual and multilingual settings. |
Leveraging Similar Users for Personalized Language Modeling with Limited Data (2022.acl-long)
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| Challenge: | Recent work suggests that personalized models are more accurate for individual users than one-size-fits-all solutions. |
| Approach: | They propose a model trained on users that are similar to a new user to find similarity between new and existing users. |
| Outcome: | The proposed model can predict what a user will write when they join a platform and not enough text is available. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |