arXiv AI
Target-Aware Curation of Hallucination Stress Tests for 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
1d ago
Mycelium: A Meta-Study of Hallucination Detection Across Provenance, Typology, and Granularities
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.
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 Computation and Language
Oct 21
Fluent but Fallible: Benchmarking Hallucination and Factual Reliability in Large Language Models
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.
arXiv Computation and Language
Oct 21
Detecting Errors in Large Language Model Responses: The Incremental Predictive Value of Hidden States
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.
arXiv AI
Jun 16
A Unified Definition of Hallucination: It's The World Model, Stupid!
arXiv:2512. 21577v3 Announce Type: replace-cross Abstract: Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs.