13 papers
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…
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…
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…
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…