arXiv AI June 2, 2026 By Shamanth Kuthpadi Seethakantha, Dung Ngoc Thai, Vara Prasad Gudi, Simran Tiwari, Rami Matar, Avijit Mitra, Wenlong Zhao, Andrew McCallum, Wael Salloum

Hallucination Detection-Guided Preference Optimization for Clinical Summarization

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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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.

By Suhana Bedi, Bridget Lin, Anson Y. Zhou, Chloe O. Stanwyck, Jenelle A. Jindal, Sanmi Koyejo, David Stutz, Nigam H. Shah

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LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization

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...

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Jun 15

ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning

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Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration

arXiv:2509. 21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples.

By Dongkyu Cho, Miao Zhang, Rumi Chunara

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Jun 3

Conformal Language Modeling via Posterior Sampling

arXiv:2606. 03731v1 Announce Type: new Abstract: Large Language Models remain plagued by hallucinations.

By Nicolas Emmenegger, Theo X. Olausson, Armando Solar-Lezama, Chara Podimata

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arXiv AI

Jun 8

When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations

arXiv:2606. 07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization.

By Mahdi Alkaeed

llms nlp benchmarks safety

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CARE: A Conformal Safety Layer for Medical Summarization

LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization

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