The paper introduces a trilingual spoken hallucination detection benchmark covering English, Russian, and Kazakh news, with 12,013 samples that include synthetic alterations and severity levels, as well as 290 fact‑checked misinformation items. Detectors are evaluated in a reference‑free setting on text, ASR transcripts, and audio, revealing that most models underperform baseline classifiers, except Gemma‑3n on transcripts. Synthetic‑trained detectors achieve high macro‑F1 scores on real‑world misinformation, but Russian provenance analysis highlights model‑dependent signals that confound synthetic benchmarks.
By Meruyert Aristombayeva, Jason S. Lucas, Chaewan Chun, Dongwon Lee
TRILOGUE is a new trilingual benchmark for spoken dialogue fact‑checking, covering English, Russian, and Kazakh. It includes almost 12,000 dialogues, 187,000 turns, and 390 hours of paired audio with ASR transcripts and word‑level timestamps, as well as nearly 5,000 human‑recorded Russian and Kazakh files. The dataset supports tasks such as claim check‑worthiness detection, evidence retrieval, and claim verification under various input conditions, and baseline experiments reveal challenges with ASR errors and cross‑lingual transfer, especially for Kazakh.
By Chaewan Chun, Meruyert Aristombayeva, Jiyoung Choi, Mahjabin Nahar, Delvin Ce Zhang, Dongwon Lee
arXiv:2608. 16353v1 Announce Type: cross Abstract: Even well-aligned large language models confidently generate factually incorrect text, making hallucination a persistent reliability risk in high-stakes deployments.
By Zhihao Guo, Zonghan Wu, Huan Huo, DaYong Ye, Junwei Zhang, Weiran Yao, Zhiwei Liu, Qingsong Wen, Yilei Shao
The paper presents a reference‑free framework for detecting hallucinations in Sinhala‑to‑English neural machine translation. It introduces a 45,000‑sample synthetic dataset created via five linguistically motivated corruption strategies and a semantic rescue mechanism that uses character‑level similarity. Using a fine‑tuned mDeBERTa‑v3 model, the authors achieve a token‑level F1 of 0.841 and demonstrate that the detector relies on the Sinhala source, not on surface artefacts, with an AUROC of 0.970 on a source‑disjoint test set. The study also benchmarks eight NMT systems, showing that hallucination rates vary by an order of magnitude across architectures.
By Navam Obeysekara, Nevidu Jayatilleke
The paper investigates why automatic speech recognition (ASR) systems sometimes generate fluent text that does not correspond to the input audio, a phenomenon termed hallucination. By examining two independently trained Conformer‑Large models—one using CTC and the other RNN‑T—under conditions of environmental noise and speaker‑background shift, the authors identify the final encoder stage as a critical boundary. Bypassing this final block leads to divergence on almost all utterances, while bypassing earlier blocks has minimal effect; at this stage, representations become compact, the decoder can read the text, and grapheme information becomes explicit, yet the output is garbled or repetitive rather than fluent fabrication. The study thus pinpoints a mechanistic precondition for hallucination—failure to produce adequately grounded output—though it does not fully explain naturally occurring hallucinations, and it highlights a consistent terminal‑stage dependency across decoder families and distribution shifts.
By Hamees Sayed, Apoorv Singh, Kumar Aman, Akshat Mandloi
arXiv:2606. 00819v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved strong performance across diverse natural language tasks, yet their outputs often suffer from hallucinations -- content that is misaligned with factual information.
By Hanze Li, Jinhao You, Yichen Guo, Kai Tang, Shuangyang Xie, Xiande Huang
arXiv:2608.18082v2 Announce Type: replace
Abstract: Although context windows have expanded significantly in recent years, hallucinations in long-context summarization remain a challenge. Long novels...
By Ruizhi Zhang, Jinwei Chen, Xiangju Lu, He Yan, Mo Yu, Junmin Zhu, Wei Zhang
VietPrism is a newly released, large‑scale Vietnamese speech corpus that combines 993.4 hours of real utterances from 1,262 verified speakers with 3.1 k hours of synthetic spoof speech. It uniquely offers transcripts, consistent speaker identities, five dialect groups, and extensive Vietnamese‑English code‑switching—nearly half of the corpus—while pairing each spoof with a matched bona fide utterance. The dataset enables controlled evaluation of deep‑fake detection models, revealing significant variability in detector performance across dialects and speaker similarity.
By Minh Hoang, Thai Le
The paper introduces VeriSpeak, a benchmark of 3,879 spoken claims for evaluating fact verification in Large Audio Language Models (LALMs). It shows a clear modality gap: models that verify written claims well often fail on spoken versions, and retrieval alone offers limited improvement. Combining retrieval with explicit reasoning yields the best performance, reaching 86.1% accuracy and demonstrating the need for grounded reasoning over retrieved evidence in speech misinformation detection.
By Debajyoti Mazumder, Mamta, Abhirama Subramanyam Penamakuri
BabelFake is a multilingual audio‑visual DeepFake benchmark featuring 399,000 clips from 496 consenting participants across five languages (English, German, Italian, French, Spanish). The dataset pairs 11 modern video manipulation methods with 4 voice‑cloning engines, covering both visual‑only and joint audio‑visual attacks. Experiments show that detection difficulty varies with the audio‑visual pairing, with significant performance drops when authentic audio is retained, and that cross‑language and demographic factors influence detector performance.
By Carlotta Segna, Joel Tschesche, Anna Rohrbach
arXiv:2606. 02642v1 Announce Type: cross Abstract: Despite the success of audio-visual large-language models (LLMs), they can produce plausible but ungrounded outputs, termed hallucination.
By Chenshuang Zhang, Kyeong Seon Kim, Chengxin Liu, Tae-Hyun Oh
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.