arXiv Machine Learning
Jun 3
Conformal Language Modeling via Posterior Sampling
arXiv:2606. 03731v1 Announce Type: new Abstract: Large Language Models remain plagued by hallucinations.
Conformal Language Modeling via Posterior Sampling
Large Language Models remain plagued by hallucinations. Recent work has sought to tame their prevalence using statistical techniques based on conformal prediction, with both theoretical and empirical success.
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arXiv AI
Jun 2
Hallucination Detection-Guided Preference Optimization for Clinical Summarization
arXiv:2605. 28910v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications.
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 2
Domain-Shift-Aware Conformal Prediction for Large Language Models
arXiv:2510. 05566v2 Announce Type: replace-cross Abstract: Large language models have achieved impressive performance across diverse tasks.
arXiv AI
Jun 9
CARE: A Conformal Safety Layer for Medical Summarization
arXiv:2606. 08969v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims.
arXiv AI
Jul 7
Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs
arXiv:2607. 03426v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions required in sequential decision-making settings.
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.
arXiv AI
Jun 9
Evaluating Hallucinations in Domain-Adapted Large Language Models
arXiv:2606. 07521v1 Announce Type: cross Abstract: This study investigates the phenomenon of hallucinations in domain-adapted Large Language Models (LLMs), focusing on the fine-tuning of the Llama-2 model with the Lamini dataset.
arXiv AI
Jun 17
MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation
arXiv:2606. 17449v1 Announce Type: cross Abstract: While Multimodal Retrieval-Augmented Generation (M-RAG) enhances Large Vision-Language Models, it remains highly susceptible to cross-modal hallucinations, causal fabrications, and sycophancy.
arXiv AI
Jun 29
From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models
arXiv:2606. 27679v1 Announce Type: cross Abstract: Probe-based uncertainty estimation (UE) has emerged as a prominent approach to detect hallucinations in Large Language Models (LLMs) by learning uncertainty from internal model signals.
arXiv AI
Jul 21
Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain
arXiv:2603. 21693v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating responses that contradict the input image, posing serious risks in clinical settings.
arXiv Machine Learning
Jul 14
Hallucination Detection in Large Language Models Using Diversion Decoding
arXiv:2607. 10476v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as a powerful tool for retrieving knowledge through seamless, human-like interactions.