The paper surveys hallucinations in large language models (LLMs) through a lifecycle lens, covering causes, detection, mitigation, and prevention. It categorizes hallucinations into data‑related, training‑related, and inference‑related stages, aligning each with specific interventions. The authors also review benchmark datasets and propose a standardized framework to diagnose and address hallucinations for safer, more reliable LLMs.
By Naveen Lamba, Sanju Tiwari, Manas Gaur
arXiv:2603. 09986v3 Announce Type: replace-cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to mitigate against.
By Brandon C. Colelough, Davis Bartels, Dina Demner-Fushman
The paper examines a specific type of hallucination in large language models caused by spurious correlations—unintended, statistically prominent associations in training data such as surnames linked to nationalities. These hallucinations are confidently produced, persist regardless of model scaling or refusal fine‑tuning, and evade existing detection methods like confidence filtering and inner‑state probing. The authors use controlled synthetic experiments and evaluations on both open‑source and proprietary LLMs, including GPT‑5, to demonstrate the failure of current detection techniques and provide a theoretical explanation for why statistical biases undermine confidence‑based approaches.
By Shaowen Wang, Yiqi Dong, Ruinian Chang, Tansheng Zhu, Yuebo Sun, Kaifeng Lyu, Jian Li
arXiv:2610.27247v1 Announce Type: cross
Abstract: Hallucination stress tests can yield similar average scores across language models even when different prompts elicit hallucinations in each model. W...
By Jiho Lee, Bingquan Zhang, Lin Ting Liang, Soroush Vosoughi, Weicheng Ma
arXiv:2606. 14697v1 Announce Type: cross Abstract: Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support.
By Sicheng Yang, Hangjie Yuan, Wenjun Zhang, Jinwang Wang, Yichen Qian, Weihua Chen, Fan Wang, Lei Zhu
arXiv:2604.02543v2 Announce Type: replace
Abstract: As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust thei...
By Ji Young Byun, Young-Jin Park, Jean-Philippe Corbeil, Asma Ben Abacha