The paper presents a meta‑study of hallucination detection in large language models, framing it as a benchmark‑conditioned measurement problem. It organizes existing benchmarks along three axes—provenance, typology, and granularity—to analyze how construction, annotation, and evaluation choices influence detection tasks. The authors also examine span‑level evaluation, propose a new Span Coverage F1 metric, and discuss the implications for cross‑benchmark comparisons and generalization.
By Julia Belikova, Elisei Rykov
arXiv:2602.11166v2 Announce Type: replace-cross
Abstract: Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assume...
By Xu Hu, Yifan Zhang, Songtao Wei, Chen Zhao, Qiannan Li, Bingzhe Li, Feng Chen
The paper introduces a multi‑signal pipeline for detecting hallucinations in large language models, combining fine‑tuned DeBERTa‑v3 classification, Monte Carlo Dropout uncertainty, and temperature‑scaled calibration. On the HaluEval benchmark it achieves high performance (F1 = 0.915, AUROC = 0.977) across QA, summarization, and dialogue, and shows that 25 % of training data yields 77 % of full‑data performance. The authors also demonstrate that applying Direct Preference Optimization to a Qwen2.5‑0.5B generator cuts hallucination rates from 85.5 % to 37.7 %, and that domain‑specific fine‑tuning (PubMedBERT on SciFact) outperforms general‑domain models for biomedical text.
By Varun Teja Chundru, Debasmita Biswas
arXiv:2509.09658v3 Announce Type: replace
Abstract: Hallucinations in multimodal large language models (MLLMs), where a model generates content inconsistent with the input image, pose significant ris...
By Bingkui Tong, Jiaer Xia, Sifeng Shang, Kaiyang Zhou
The paper introduces a multi‑signal pipeline for detecting hallucinations in large language model outputs, combining fine‑tuned DeBERTa‑v3 classification, Monte Carlo Dropout uncertainty, and temperature‑scaled calibration. On the HaluEval benchmark it achieves strong performance (F1 = 0.915, AUROC = 0.977) and further improves accuracy to 93.2% with MC Dropout. The authors also demonstrate that applying Direct Preference Optimization to a Qwen2.5‑0.5B generator reduces hallucination rates from 85.5% to 37.7%, and show that domain‑specific fine‑tuning (PubMedBERT on SciFact) yields better results than general‑domain training.
The paper investigates whether the internal hidden states of large language models can improve the detection of hallucinations in model responses. By integrating hidden states with an output‑side baseline and evaluating on both open‑ended generation and multiple‑choice tasks, the authors find that adding hidden states yields modest AUROC gains of about 0.004 and 0.039. These results suggest that while hidden states contain additional predictive information, the overall improvement over strong standalone detection methods remains limited.
By Ningzhi Li, Xiaojun Ma, Xiaofei Wang
arXiv:2601.19918v2 Announce Type: replace
Abstract: Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in...
By Yitong Qiao, Licheng Pan, Yu Mi, Lei Liu, Yue Shen, Jian Wang, Jinjie Gu, Fei Sun, Zhixuan Chu
arXiv:2609.35804v1 Announce Type: cross
Abstract: Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, leading to their widespread deployment...
By Mamehgol Yousefi, Ahmad Shahi, Mos Sharifi, Alvaro Romera, Simon Hoermann, Tham Piumsomboon
arXiv:2606.21359v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly used to communicate and explain scientific concepts, yet their tendency to hallucinate poses signific...
By Raia Abu Ahmad, Nikolas Rauscher, Ekaterina Borisova, Fabio Barth, Georg Rehm, Sebastian M\"oller
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
The paper "Fluent but Fallible: Benchmarking Hallucination and Factual Reliability in Large Language Models" evaluates ten advanced LLMs on a benchmark of 70 academic prompts across four complexity levels. Using five metrics—Accuracy, Precision, Recall, Response Deviation Index, and Hallucination Index—reviewers found that Perplexity and DeepSeek V3 achieved the highest overall accuracy, Gemini balanced recall and performance, LLaMA 3 resisted hallucinations, and Falcon had perfect precision but low recall and high deviation. The study highlights a trade‑off between response completeness and factual reliability, especially as prompt complexity increases.
By Osama Shabih, Dayam Nadeem, Shahab Saquib Sohail, Mohammad Nadeem
arXiv:2606. 00301v1 Announce Type: new Abstract: Hallucinations in large language models (LLMs) arise from heterogeneous failure mechanisms, making reliable detection difficult for any single global uncertainty score.
By Wentao Ye, Liyao Li, Zhiqing Xiao, Muzhi Zhu, Jiaqi Hu, Zhanming Shen, Xiaomeng Hu, Sean Du, Haobo Wang