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Dedicated to studies primarily investigating the causes, implications, and solutions for the phenomenon where language models generate plausible but incorrect or nonsensical outputs.
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 When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs Shaowen Wang Yiqi Dong Ruinian Chang Tansheng Zhu Yuebo Sun Kaifeng Lyu Jian Li | |||
 When Evidence Contradicts: Toward Safer Retrieval-Augmented Generation in Healthcare Saeedeh Javadi Sara Mirabi Manan Gangar Bahadorreza Ofoghi | |||
 Stress Testing Factual Consistency Metrics for Long-Document Summarization Zain Muhammad Mujahid Dustin Wright Isabelle Augenstein | |||
 NOAH: Benchmarking Narrative Prior driven Hallucination and Omission in Video Large Language Models Kyuho Lee Euntae Kim Jinwoo Choi Buru Chang | |||
 Injecting Falsehoods: Adversarial Man-in-the-Middle Attacks Undermining Factual Recall in LLMs Alina Fastowski Bardh Prenkaj Yuxiao Li Gjergji Kasneci | |||
 Stemming Hallucination in Language Models Using a Licensing Oracle Simeon Emanuilov Richard Ackermann | |||
 CPR: Mitigating Large Language Model Hallucinations with Curative Prompt RefinementIEEE International Conference on Systems, Man and Cybernetics (SMC), 2024 | |||
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