arXiv AI August 5, 2026 By Brandon C. Colelough, Davis Bartels, Dina Demner-Fushman

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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.

By Sanchita Porwal, Sai Prasath S, Xingjian Bi, Madelyn Scandlen

llms fine-tuning safety

arXiv AI

Jun 2

BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali

arXiv:2605. 31483v1 Announce Type: cross Abstract: Despite Bengali being the sixth most spoken language in the world, no prior work has systematically evaluated hallucination in large language models (LLMs) for Bengali.

By Shefayat E Shams Adib, Ahmed Alfey Sani, Ekramul Alam Esham, Ajwad Abrar, Ishmam Tashdeed, Md Taukir Azam Chowdhury

llms nlp benchmarks safety

arXiv AI

Jul 17

Contextualized Evaluation of Vision Language Models through Dynamic, Multi-turn Interactions

arXiv:2607. 14499v1 Announce Type: new Abstract: Multi-modal Large Language Models (MLLMs) have made substantial advances on benchmarks, yet their real-world effectiveness remains uncertain.

By Yijiang Li, Huiqi Zou, Bingyang Wang, Ziang Xiao

llms multimodal benchmarks safety

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.

By Emmy Liu, Varun Gangal, Chelsea Zou, Michael Yu, Xiaoqi Huang, Alex Chang, Zhuofu Tao, Karan Singh, Sachin Kumar, Steven Y. Feng

llms rag benchmarks safety

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.

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

llms safety

arXiv AI

Aug 11

When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information

arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.

By Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam, Quan Z. Sheng

llms nlp

Related stories

Evaluating Hallucinations in Domain-Adapted Large Language Models

BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali

Contextualized Evaluation of Vision Language Models through Dynamic, Multi-turn Interactions

A Unified Definition of Hallucination: It's The World Model, Stupid!

Conformal Language Modeling via Posterior Sampling

When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information