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cs.CL2026
What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection
Lei Jiang, Yue Zhou, Natalie Parde
Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data. While large language models (LLMs) have shown str…
cs.CL2026
HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering
Chaodong Tong, Qi Zhang, Zhuojun Jiang +2
Large language models (LLMs) achieve strong question answering (QA) performance but can produce fluent answers unsupported by available evidence. Existing hallucination detectors o…
cs.CL2026
Knowledge Dependency Estimation for Reliable Question Answering
Chaodong Tong, Qi Zhang, Nannan Sun +2
Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In realistic LLM-based QA, thi…