Related papers: Are Hallucinations Bad Estimations?

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Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing [3.4782736103257323]
Large language models often hallucinate with high confidence on "random facts"<n>We formalize memorization of such facts as a membership testing problem.<n>We show that hallucinations persist as a natural consequence of lossy compression.
arXiv Detail & Related papers (2026-01-31T21:18:28Z)
Counting Hallucinations in Diffusion Models [34.45858211220468]
Diffusion probabilistic models (DPMs) have demonstrated remarkable progress in generative tasks, such as image and video synthesis.<n>They often produce hallucinated samples (hallucinations) that conflict with real-world knowledge.<n>Despite their prevalence, the lack of feasible methodologies for systematically quantifying such hallucinations hinders progress.
arXiv Detail & Related papers (2025-10-15T01:48:04Z)
Hallucination is Inevitable for LLMs with the Open World Assumption [10.473344768196908]
Large Language Models (LLMs) exhibit impressive linguistic competence but also produce inaccurate or fabricated outputs, often called hallucinations''<n>This paper reframes hallucination'' as a manifestation of the generalization problem.
arXiv Detail & Related papers (2025-09-29T13:38:44Z)
Review of Hallucination Understanding in Large Language and Vision Models [65.29139004945712]
We present a framework for characterizing both image and text hallucinations across diverse applications.<n>Our investigations reveal that hallucinations often stem from predictable patterns in data distributions and inherited biases.<n>This survey provides a foundation for developing more robust and effective solutions to hallucinations in real-world generative AI systems.
arXiv Detail & Related papers (2025-09-26T09:23:08Z)
Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models [0.0]
We propose Counterfactual Probing, a novel approach for detecting and mitigating hallucinations in large language models.<n>Our method dynamically generates counterfactual statements that appear plausible but contain subtle factual errors, then evaluates the model's sensitivity to these perturbations.
arXiv Detail & Related papers (2025-08-03T17:29:48Z)
Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models [8.97308732968526]
We study the causality of hallucinations under constrained knowledge domains by auditing the Chain-of-Thought trajectory.<n>Our analysis reveals that in long-CoT settings, RLLMs can iteratively reinforce biases and errors through flawed reflective reasoning.<n>Surprisingly, even direct interventions at the origin of hallucinations often fail to reverse their effects.
arXiv Detail & Related papers (2025-05-19T14:11:09Z)
Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer [51.7407540261676]
We investigate a distinct type of hallucination, where a model can consistently answer a question correctly, but a seemingly trivial perturbation causes it to produce a hallucinated response with high certainty.<n>This phenomenon is particularly concerning in high-stakes domains such as medicine or law, where model certainty is often used as a proxy for reliability.<n>We show that CHOKE examples are consistent across prompts, occur in different models and datasets, and are fundamentally distinct from other hallucinations.
arXiv Detail & Related papers (2025-02-18T15:46:31Z)
HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection [16.27352940098609]
We propose a new data set, HalluEntity, which annotates hallucination at the entity level.<n>Based on the dataset, we evaluate uncertainty-based hallucination detection approaches across 17 modern LLMs.<n>Our experimental results show that uncertainty estimation approaches focusing on individual token probabilities tend to over-predict hallucinations.
arXiv Detail & Related papers (2025-02-17T16:01:41Z)
Valuable Hallucinations: Realizable Non-realistic Propositions [2.451326684641447]
This paper introduces the first formal definition of valuable hallucinations in large language models (LLMs)<n>We focus on the potential value that certain types of hallucinations can offer in specific contexts.<n>We present experiments using the Qwen2.5 model and HalluQA dataset, employing ReAct prompting to control and optimize hallucinations.
arXiv Detail & Related papers (2025-02-16T12:59:11Z)
Hallucination, Monofacts, and Miscalibration: An Empirical Investigation [2.3278261859840104]
Hallucinated facts in large language models obey a statistical lower bound determined by the monofact rate.<n>We present the first empirical investigation of this three-way relationship in classical n-gram models and fine-tuned encoder-decoder Transformers.
arXiv Detail & Related papers (2025-02-11T18:46:00Z)
Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models [65.32990889402927]
We coin this phenomenon as knowledge overshadowing'' We show that the hallucination rate grows with both the imbalance ratio and the length of dominant condition description. We propose to utilize overshadowing conditions as a signal to catch hallucination before it is produced.
arXiv Detail & Related papers (2024-07-10T20:37:42Z)
On Large Language Models' Hallucination with Regard to Known Facts [74.96789694959894]
Large language models are successful in answering factoid questions but are also prone to hallucination. We investigate the phenomenon of LLMs possessing correct answer knowledge yet still hallucinating from the perspective of inference dynamics. Our study shed light on understanding the reasons for LLMs' hallucinations on their known facts, and more importantly, on accurately predicting when they are hallucinating.
arXiv Detail & Related papers (2024-03-29T06:48:30Z)
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data [102.56792377624927]
hallucinations inherent in machine-generated data remain under-explored. We present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. Our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA.
arXiv Detail & Related papers (2023-11-22T04:52:58Z)
Mutual Information Alleviates Hallucinations in Abstractive Summarization [73.48162198041884]
We find a simple criterion under which models are significantly more likely to assign more probability to hallucinated content during generation: high model uncertainty. This finding offers a potential explanation for hallucinations: models default to favoring text with high marginal probability, when uncertain about a continuation. We propose a decoding strategy that switches to optimizing for pointwise mutual information of the source and target token--rather than purely the probability of the target token--when the model exhibits uncertainty.
arXiv Detail & Related papers (2022-10-24T13:30:54Z)
On Hallucination and Predictive Uncertainty in Conditional Language Generation [76.18783678114325]
Higher predictive uncertainty corresponds to a higher chance of hallucination. Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties. It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant.
arXiv Detail & Related papers (2021-03-28T00:32:27Z)

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