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Zhouhong Gu

ACL 2026×3NeurIPS 2026评测与基准安全检索与知识社会与人本推理
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0 一作 · 4 篇 · ACL 2026 / NeurIPS 2026

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2026 顶会论文

4
作者位次 #2/5ACL 2026Poster· Natural Language Processing

PII -Bench: Evaluating Query-Aware Privacy Protection Systems

Hao Shen · Zhouhong Gu · Haokai Hong · Weili Han · Hongfeng Chai

摘要

The widespread adoption of Large Language Models (LLMs) has raised significant privacy concerns regarding the exposure of personally identifiable information (PII) in user prompts. To address this challenge, we propose a query-unrelated PII masking strategy and introduce PII-Bench, the first comp...

安全评测与基准OpenReview 项目页
作者位次 #4/9ACL 2026Poster· Natural Language Processing

The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models

Ying He · Sihang Jiang · Xingzhou Chen · Zhouhong Gu · Yiwei Gu · Minggui HE · Shimin Tao · Mahongxia · Yanghua Xiao

摘要

Cultural taboo safety is essential for deploying large language models (LLMs), as culturally insensitive outputs may cause offense or even social harm. However, existing cultural benchmarks primarily assess cultural knowledge or values biases, while overlooking whether LLMs can recognize and resp...

社会与人本安全评测与基准OpenReview 项目页
作者位次 #2/11ACL 2026Findings· Findings of ACL

Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents

Ying He · Zhouhong Gu · Zhecheng Hu · Yubo Zhou · Hao Shen · Jiaqing Liang · Zhaoqian Dai · Ma Shuguang · F. Richard Yu · Yanghua Xiao · Zhixu Li

摘要

Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However,...

评测与基准推理强化学习与后训练OpenReview 项目页

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