2 citations · 8 across the 15 of their papers we have counts for
16 papers
Fin_Agent at FinEval 2026: Task-Aware Methods for Multilingual Financial NLP
Mohotarema Rashid, Tanzina Hossain, Xiaoying Song +1
We present Fin_Agent, our submission to the FinEval 2026 shared task on multilingual fi nancial language understanding. Our sys tems address Greek financial named-entity recognitio…
Ask or Answer: A Decision Framework for Multi-Turn Health Misinformation Intervention
Xiaoying Song, Anirban Saha Anik, Jinyu Liu +3
Correcting health misinformation in dialogue requires more than producing a factual rebuttal: users differ in what they know, what they believe, and what they need to hear, so an e…
SourceMinds at CheckThat! 2026: NLI-Grounded Citation Auditing in a Multi-Agent Pipeline for Full Fact-Checking Article Generation
Farhan Sharukh Hasan, Anirban Saha Anik, Eric Liu +3
This paper presents our system for Task 3 of the CLEF 2026 CheckThat! Lab, which focuses on generating full fact-checking articles from claims, veracity labels, and evidence docume…
SciClaimSeekers at CheckThat! 2026: Retrieving Scientific Sources for Social Media Claims with LLM Reranking
Mohotarema Rashid, Nansu Baniya, Anirban Saha Anik +2
Scientific claims often spread on social media faster than they can be verified, while posts rarely link to the original scholarly sources. To tackle this problem this paper presen…
Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs
Qitao Tan, Xiaoying Song, Arman Akbari +7
Current safety alignment of foundation models largely follows a \emph{one-size-fits-all} paradigm, applying the same refusal policy across users and contexts. As a result, models m…
Q-realign: Piggybacking Realignment on Quantization for Safe and Efficient LLM Deployment
Qitao Tan, Xiaoying Song, Ningxi Cheng +6
Public large language models (LLMs) are typically safety-aligned during pretraining, yet task-specific fine-tuning required for deployment often erodes this alignment and introduce…
End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost
Qitao Tan, Xiaoying Song, Jin Lu +9
Quantization is an effective technique to reduce the deployment cost of large language models (LLMs), and post-training quantization (PTQ) has been widely studied due to its effici…
Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL
Xiaoying Song, Anirban Saha Anik, Dibakar Barua +3
Health misinformation spreading online poses a significant threat to public health. Researchers have explored methods for automatically generating counterspeech to health misinform…
A Dynamic Fusion Model for Consistent Crisis Response
Xiaoying Song, Anirban Saha Anik, Eduardo Blanco +2
In response to the urgent need for effective communication with crisis-affected populations, automated responses driven by language models have been proposed to assist in crisis co…
Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
Anirban Saha Anik, Xiaoying Song, Elliott Wang +3
Large language models (LLMs) incorporated with Retrieval-Augmented Generation (RAG) have demonstrated powerful capabilities in generating counterspeech against misinformation. Howe…
A Hybrid Framework for Subject Analysis: Integrating Embedding-Based Regression Models with Large Language Models
Jinyu Liu, Xiaoying Song, Diana Zhang +3
Providing subject access to information resources is an essential function of any library management system. Large language models (LLMs) have been widely used in classification an…
Echoes of Discord: Forecasting Hater Reactions to Counterspeech
Xiaoying Song, Sharon Lisseth Perez, Xinchen Yu +2
Hate speech (HS) erodes the inclusiveness of online users and propagates negativity and division. Counterspeech has been recognized as a way to mitigate the harmful consequences. W…
Assessing the Human Likeness of AI-Generated Counterspeech
Xiaoying Song, Sujana Mamidisetty, Eduardo Blanco +1
Counterspeech is a targeted response to counteract and challenge abusive or hateful content. It effectively curbs the spread of hatred and fosters constructive online communication…
Outcome-Constrained Large Language Models for Countering Hate Speech
Lingzi Hong, Pengcheng Luo, Eduardo Blanco +1
Automatic counterspeech generation methods have been developed to assist efforts in combating hate speech. Existing research focuses on generating counterspeech with linguistic att…
Hate Cannot Drive out Hate: Forecasting Conversation Incivility following Replies to Hate Speech
Xinchen Yu, Eduardo Blanco, Lingzi Hong
User-generated replies to hate speech are promising means to combat hatred, but questions about whether they can stop incivility in follow-up conversations linger. We argue that ef…
Hate Speech and Counter Speech Detection: Conversational Context Does Matter
Xinchen Yu, Eduardo Blanco, Lingzi Hong
Hate speech is plaguing the cyberspace along with user-generated content. This paper investigates the role of conversational context in the annotation and detection of online hate…