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cs.CL2026
Reducing Hallucinations in LLMs via Factuality-Aware Preference Learning
Sindhuja Chaduvula, Ahmed Y. Radwan, Azib Farooq +2
Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce hallucinations when preference judgmen…
cs.CL2026
Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods
Shaina Raza, Rizwan Qureshi, Azib Farooq +4
Large language models (LLMs) reproduce misinformation not by memorizing false facts alone, but by learning the linguistic patterns that make falsehoods persuasive, such as hedging,…
cs.CL2025
FairSense-AI: Responsible AI Meets Sustainability
Shaina Raza, Mukund Sayeeganesh Chettiar, Matin Yousefabadi +2
In this paper, we introduce FairSense-AI: a multimodal framework designed to detect and mitigate bias in both text and images. By leveraging Large Language Models (LLMs) and Vision…