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The Hallucinations Leaderboard, an Open Effort to Measure Hallucinations in Large Language Models
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arXiv AI
Aug 5
Quantifying Hallucinations in Language Language Models on Medical Textbooks
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.
arXiv AI
Jun 6
The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?
arXiv:2504. 10020v4 Announce Type: replace-cross Abstract: Contrastive decoding strategies are widely used to reduce object hallucinations in multimodal large language models (MLLMs).
arXiv Machine Learning
Sep 14
When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs
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.
arXiv Machine Learning
Aug 4
Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
arXiv:2604. 10697v2 Announce Type: replace-cross Abstract: Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context.
Hugging Face Trending Papers
Aug 26
Overview of SHROOM-Visions 2026: A Shared Task on Hallucination Detection in Large Vision-Language Models
In 2026, we held the fourth iteration of the SHROOM Shared Task series: SHROOM-Visions (\textbf{S}hared-task on \textbf{H}allucinations and \textbf{R}elated \textbf{O}bservable \textbf{O}vergeneration...
arXiv AI
5d ago
How Do Uncertainty Estimators Align with Hallucination Occurrence in LLMs?
arXiv:2605.27016v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can hallucinate, but do model uncertainty estimators reliably reveal when they do? Although uncertainty estimato...