Hugging Face Trending Papers June 22, 2026

Have You Ever Seen Them? Entity-level Membership Inference through Interrogating Large Language Models

Read the original on Hugging Face Trending Papers →

Large Language Models (LLMs) raise growing concerns about privacy leakage and copyright compliance. Membership inference is a key tool for assessing such risks, but existing studies mainly focus on whether specific samples or sample-based data units are used for training.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

5d ago

A Prior-Aware Metric for Efficiently Distinguishing Memorization from Generalization in Large Language Models

arXiv:2602. 18733v2 Announce Type: replace Abstract: Training data leakage from Large Language Models (LLMs) raises serious concerns related to privacy, security, and copyright compliance.

By Trishita Tiwari, Ari Trachtenberg, G. Edward Suh

llms safety

arXiv AI

Jun 8

Auditing Training Data in Domain-adapted LLMs: LoRA-MINT

arXiv:2606. 06946v1 Announce Type: cross Abstract: We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Natural Language Processing (NLP) tasks through Low-Rank Adaptation (LoRA).

By Gonzalo Mancera, Daniel DeAlcala, Aythami Morales, Julian Fierrez, Ruben Tolosana, Francisco Jurado

llms nlp fine-tuning benchmarks

arXiv AI

Jul 22

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

arXiv:2606. 16952v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.

By Kareem Amin, Rudrajit Das, Alessandro Epasto, Adel Javanmard, Dennis Kraft, M\'onica Ribero, Sergei Vassilvitskii

llms safety

arXiv Machine Learning

Jul 21

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

arXiv:2310. 16152v5 Announce Type: replace-cross Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.

By Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Kang Gu, Najrin Sultana, Shagufta Mehnaz

llms nlp safety

Hugging Face Trending Papers

Aug 11

Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection

Large language models (LLMs) are trained on massive and largely undisclosed corpora that may contain copyrighted or privacy-sensitive content. Data contamination detection (DCD) therefore aims to determine whether a given text is a member of the pre-training corpus of a target LLM.

llms fine-tuning benchmarks safety

arXiv AI

1d ago

SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling

arXiv:2508. 09105v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) and its Multimodal Retrieval-Augmented Generation (MRAG) significantly improve the knowledge coverage and contextual understanding of Large Language Models (LLMs) by introducing external knowledge sources.

By Shixuan Sun, Siyuan Liang, Jianjie Huang, Jingzhi Li, Xiaochun Cao

llms rag multimodal safety

Related stories

A Prior-Aware Metric for Efficiently Distinguishing Memorization from Generalization in Large Language Models

Auditing Training Data in Domain-adapted LLMs: LoRA-MINT

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection

SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling