arXiv AI August 6, 2026 By Anran Li, Lingfei Qian, Mengmeng Du, Yu Yin, Yan Hu, Zihao Sun, Yihang Fu, Hyunjae Kim, Erica Stutz, Xuguang Ai, Qianqian Xie, Rui Zhu, Jimin Huang, Yifan Yang, Siru Liu, Yih-Chung Tham, Lucila Ohno-Machado, Hyunghoon Cho, Zhiyong Lu, Hua Xu, Qingyu Chen

Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications

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arXiv:2509. 08604v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to enhance domain-specific accuracy and safety.

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

arXiv AI

Jun 2

On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance

arXiv:2606. 00467v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions.

By Etienne Casanova, Rafal Kocielnik, R. Michael Alvarez

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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

arXiv AI

Jun 8

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.

By Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra, Sandipan Sikdar, Koustav Rudra

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arXiv AI

Aug 11

LLMs Remember First, Forget Last: Dual-Process Interference in Large Language Models

arXiv:2603. 00270v3 Announce Type: replace-cross Abstract: Large language models can process millions of tokens, yet how they handle conflicting information within context remains poorly understood.

By Sourav Chattaraj, Kanak Raj

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arXiv AI

Jun 9

From `May' to `Is': Certainty Distortion in Language Model Rewriting

arXiv:2606. 07951v1 Announce Type: cross Abstract: Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports.

By Catarina G Belem, Shang Wu, Hongyu Yao, Mark Steyvers, Sameer Singh, Padhraic Smyth

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arXiv AI

Jun 19

LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data

arXiv:2606. 19509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to structured clinical data, yet whether they can recognize the limits of their own knowledge on such tasks remains unexplored.

By Akshat Dasula, Prasanna Desikan, Jaideep Srivastava

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Related stories

On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance

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

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

LLMs Remember First, Forget Last: Dual-Process Interference in Large Language Models

From `May' to `Is': Certainty Distortion in Language Model Rewriting

LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data