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Kui Ren

ACL 2026×4CCS 2026×3AAAI 2026×2NDSS 2026×2VLDB 2026×2CVPR 2026ICLR 2026安全效率与系统数据与预训练评测与基准检索与知识
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0 一作 · 15 篇 · ACL 2026 / CCS 2026 / AAAI 2026 / NDSS 2026 / VLDB 2026 / CVPR 2026 / ICLR 2026

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2026 顶会论文

15
作者位次 #10/10ACL 2026Poster· Natural Language Processing

APPSI -139: A Parallel Corpus of E nglish Application Privacy Policy Summarization and Interpretation

Pengyun Zhu · Qiheng Sun · Long Wen · Yanbo Wang · YANG CAO · Junxu Liu · Deyi Xiong · Jinfei Liu · Zhibo Wang · Kui Ren

摘要

Privacy policies are essential for users to understand how service providers handle their personal data. However, these documents are often long and complex, as well as filled with technobabble and legalese, causing users to unknowingly accept terms that may even contradict the law. While summari...

数据与预训练安全可解释性OpenReview 项目页
作者位次 #8/8ACL 2026Poster· Natural Language Processing

Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training

Lei Liu · Hao Zhu · Xiaoyan Yang · YUE SHEN · Zhixuan Chu · Jian Wang · Jinjie Gu · Kui Ren

摘要

Continual Pre-training (CPT) serves as a fundamental approach for adapting foundation models to domain-specific applications. Scaling laws for pre-training define a power-law relationship between dataset size and the test loss of an LLM. However, the marginal gains from simply increasing data for...

数据与预训练效率与系统评测与基准OpenReview 项目页
作者位次 #6/6ACL 2026Findings· Findings of ACL

H int P ilot: LLM -based Compiler Hint Synthesis for Code Optimization

Hanyun Jiang · Peisen Yao · Kaiyue Li · Tingting Lin · Chengpeng Wang · Kui Ren

摘要

Code optimization remains a core objective in software development, yet modern compilers struggle to navigate the enormous optimization spaces. While recent research has looked into employing large language models (LLMs) to optimize source code directly, these techniques can introduce semantic er...

代码检索与知识推理OpenReview 项目页
作者位次 #7/7ACL 2026Findings· Findings of ACL

Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models

Weiwei Qi · Zefeng Wu · Tianhang Zheng · Zikang Zhang · Xiaojun Jia · Zhan Qin · Kui Ren

摘要

Ensuring Large Language Model (LLM) safety is crucial, yet the lack of a clear understanding about safety mechanisms hinders the development of precise and reliable methodologies for safety intervention across diverse tasks. To better understand and control LLM safety, we propose the Expected Saf...

对齐与偏好安全效率与系统OpenReview 项目页

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