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新论文 · v1arXiv:2611.04941

Residual Attribute Leakage Is an Interface: Auditing Privacy Evaluation After Text De-identification

Tuan Thanh Duong, Joongmin Shin, Jeongho Yoon, Junyoung Son, Heuiseok Lim

原文摘要

A privacy evaluation of de-identified text asks a language model (the adversary) to guess a person's protected attribute. We introduce a prior-normalised clipped leakage score of its confidence gain on the true value and audit the conclusions drawn from it. Each verdict depends on an evaluation interface (the queried person, answer options, prior, adversary model and scoring rule), which we vary on fixed releases of three corpora. In a stress test, an editor that writes the opposite-sex form at the same spans a redaction editor would redact misleads the adversary about most people, yet ranks first among editors scored on the same MedQA entries under this score, as under attack accuracy and proper scoring rules read as privacy. A relabelling-invariant reading of AUC ranks that editor sixth of eight, and a rule-aware adversary recovers the attribute from the same text. On ordinary releases most verdict changes are gains or losses of separability; of six choices varied over all pairs, only the adversary reverses comparisons (5 of its 124 changes), and the query can target the wrong person. SACC, a traceable editor we build as the instrument, lets us ablate two editing principles and show where its advantage does not hold.

cs.CL · cs.CR

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2049ACL Anthology · ACL 2026

Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding

Jaehyun Jeon NC AI, Min Soo Kim Yonsei University, Janghan Yoon, Sumin Shim Yonsei University, Yejin Choi, Hanbin Kim Yonsei University, Dae Hyun Kim Yonsei University, Youngjae Yu Seoul National University, Seoul National University and Yonsei University

原文摘要

User interface (UI) design goes beyond visuals to shape user experience (UX), underscoring the shift toward UI/UX as a unified concept. While recent studies have explored UI evaluation using Multimodal Large Language Models (MLLMs), they largely focus on surface-level features, overlooking how design choices influence user behavior at scale. To fill this gap, we introduce WiserUI-Bench, a novel benchmark for multimodal understanding of how UI/UX design affects user behavior, built on 300 real-world UI image pairs from industry A/B tests, with empirically validated winners that induced more user actions. For future design progress in practice, post-hoc understanding of why such winners succeed with mass users is also required; we support this via expert-curated key interpretations for each instance. Experiments across multiple MLLMs on WiserUI-Bench for two main tasks, (1) predicting the more effective UI image between an A/B-tested pair, and (2) explaining it post-hoc in alignment with expert interpretations, show that models exhibit limited understanding of the behavioral impact of UI/UX design. We believe our work will foster research on leveraging MLLMs for visual design in user behavior contexts.

cs.CL · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.818ACL Anthology · ACL 2026

HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering

Joongmin Shin, Gyuho Shim Korea University, Jeongbae Park Korea University, Jaehyung Seo Konkuk University, Heuiseok Lim

原文摘要

Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottlenecks: routing failure in locating the correct document and evidence fragmentation in integrating scattered information. Existing approaches relying on flat text chunks or page-level images inherently struggle to (i) precisely pinpoint the target document among thousands of candidates and (ii) organically connect multimodal evidence, such as tables and figures, within a limited token budget. To address these challenges, we propose HiKEY, a hierarchical tree-based multimodal retrieval framework that elevates document hierarchy to a first-class retrieval signal. Instead of simple chunking, HiKEY reconstructs a logical heterogeneous graph via Document Hierarchical Parsing (DHP), explicitly encoding parent–child relationships. Adopting a hierarchical coarse-to-fine strategy, the framework (1) performs global routing to rapidly prune the search space using hierarchical indexing, and (2) conducts fine-grained retrieval to rank sections by employing a multimodal fusion strategy that captures the most discriminative evidence. Finally, HiKEY assembles a token-efficient evidence subgraph via a hybrid structural-semantic packing strategy. Experiments on ODQA benchmarks demonstrate that HiKEY significantly outperforms page- and chunk-based baselines, improving retrieval recall by up to 12.9% and end-to-end QA performance by up to 6.8%.

cs.CL · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.831ACL Anthology · ACL 2026

Would LLMs be Good Historical Linguists and Chinese Dialect Learners?

Yicheng Liu, Shumin Shi Beijing Institute of Technology, Youchao Zhou, Xingchen Zhang Beijing Institute of Technology

原文摘要

Large language models (LLMs) perform well on Standard Chinese but struggle with low-resource Chinese dialects due to substantial phonological divergence. We investigate whether incorporating Middle Chinese, the common historical ancestor of most of the modern Chinese dialects, can improve dialectal pronunciation modeling in a linguistically interpretable manner. We focus on two specific task variants: (1) conditional sound change rule induction (a variant of Sound Law Induction, SLI), where models infer executable phonological transformation rules from Middle Chinese to modern dialects, and (2) sentence-level dialectal pronunciation transcription (a variant of Grapheme-to-Phoneme, G2P), requiring dialect-specific International Phonetic Alphabet (IPA) generation. We construct a multi-source dataset covering Middle Chinese and 12 modern Chinese dialects, including character-level correspondences, rule exemplars, and sentence-level IPA transcription. We adopt a parameter-efficient training framework combining LoRA-based supervised fine-tuning and reinforcement learning via Group Relative Policy Optimization (GRPO) for the first task. Across both tasks and a wide range of dialects and evaluation metrics, our approach achieves overall improvements over strong baselines, including DeepSeek-V3.2 and ChatGPT-5.2, while revealing variation across dialects. These results demonstrate the value of leveraging historical linguistic knowledge for modeling low-resource Chinese dialects.

cs.CL

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