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新论文 · v1arXiv:2611.07113

What Patients Say They Can't Access: Mining Cancer Screening Barriers from Online Communities

Ruimin Sun, Aokun Chen, Arjun Arunasalam

原文摘要

Cancer screening disparities persist across the U.S., yet the patient-reported barriers driving low uptake remain poorly characterized at scale. While clinical surveys capture structured responses, they miss the organic, unsolicited discourse where patients candidly describe real-world obstacles. In this paper, we conduct a large-scale analysis of over 56,000 posts across six cancer-specific Reddit communities to surface barriers to cancer screening among U.S. patients. We apply a pipeline combining keyword filtering, barrier triage, open coding, and codebook synthesis to systematically extract and categorize patient-reported barriers from 576 U.S.-located posts. Our qualitative analysis reveals recurring structural, informational, and psychosocial barriers that vary meaningfully by cancer type and community context. These findings lay the groundwork for future work aimed at proactively identifying and addressing barriers at scale - spanning a broader range of cancer types and populations - and informing the design of targeted interventions, navigator programs, and health system policies that translate patient-reported obstacles into actionable solutions.

cs.CL

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.385ACL Anthology · ACL 2026

Detecting RAG Extraction Attack via Dual-Path Runtime Integrity Game

Yuanbo Xie, Yingjie Zhang, Yulin Li, Shouyou Song, Xiaokun Chen Stanford University, Zhihan Liu, Liya Su, Tingwen Liu Institute of Information Engineering, Chinese Academy of Sciences

原文摘要

Retrieval-Augmented Generation (RAG) systems augment large language models with external knowledge, yet introduce a critical security vulnerability: RAG Knowledge Base Leakage, wherein adversarial prompts can induce the model to divulge retrieved proprietary content. Recent studies reveal that such leakage can be executed through adaptive and iterative attack strategies (named RAG extraction attack), while effective countermeasures remain notably lacking. To bridge this gap, we propose CanaryRAG, a runtime defense mechanism inspired by stack canaries in software security. CanaryRAG embeds carefully designed canary tokens into retrieved chunks and reformulates RAG extraction defense as a dual-path runtime integrity game. Leakage is detected in real time whenever either the target or oracle path violates its expected canary behavior, including under adaptive suppression and obfuscation. Extensive evaluations against existing attacks demonstrate that CanaryRAG provides robust defense, achieving substantially lower chunk recovery rates than state-of-the-art baselines while imposing negligible impact on task performance and inference latency. Moreover, as a plug-and-play solution, CanaryRAG can be seamlessly integrated into arbitrary RAG pipelines without requiring retraining or structural modifications, offering a practical and scalable safeguard for proprietary data.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表