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cs.CL2026
From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations
Ping Wang, Xiangguo Sun, Bingbing Xu +2
Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon we attribute to fact perturba…
cs.CL2026
Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation
Jiani Luo, Xiaoyan Zhao, Yang Zhang +4
Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interaction and shape appropriate resp…