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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
A Dynamic Fusion Model for Consistent Crisis Response
Published in The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), 2025
We introduce a dynamic fusion framework that integrates multiple LLMs to generate consistent, high-quality crisis responses across professionalism, actionability, and relevance dimensions.
Recommended citation: Song, Xiaoying, Anirban Saha Anik, Eduardo Blanco, Vanessa Frias-Martinez, and Lingzi Hong. "A Dynamic Fusion Model for Consistent Crisis Response." arXiv preprint arXiv:2509.01053 (2025).
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Dynamic Fusion of Large Language Models for Crisis Communication
Published in Proceedings of the International ISCRAM Conference, 2025
We propose a dynamic fusion framework using multiple LLMs to generate high-quality, real-time responses in crisis scenarios on social media.
Recommended citation: Hong, L., Song, X., Saha Anik, A., Frías-Martínez, V. (2025). "Dynamic Fusion of Large Language Models for Crisis Communication." Proceedings of the International ISCRAM Conference. DOI: https://doi.org/10.59297/nqysjq45.
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Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL
Published in The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025 Findings) – Accepted, 2025
We propose a Controlled-Literacy framework that integrates Retrieval-Augmented Generation (RAG) and Reinforcement Learning (RL) to generate counterspeech tailored to different health literacy levels.
Recommended citation: Song, Xiaoying, Anirban Saha Anik, Dibakar Barua, Pengcheng Luo, Junhua Ding, and Lingzi Hong. "Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL." arXiv preprint arXiv:2509.01058 (2025).</i>.
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Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
Published in Conference on Language Models (COLM 2025) – Accepted, 2025
A multi-agent LLM framework that generates evidence-based counterspeech grounded in dynamic and static retrieval sources, achieving significant improvements over RAG baselines.
Recommended citation: Anirban Saha Anik, Xiaoying Song, Elliott Wang, Bryan Wang, Bengisu Yarimbas, Lingzi Hong. (2025). "Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation." Conference on Language Models (COLM 2025).
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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