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20242026
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cs.CR2025

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment

Hao Li, Lijun Li, Zhenghao Lu +4

With rapid advancement and increasing accessibility of LLMs, fine-tuning aligned models has become a critical step for adapting them to real-world applications, which makes the saf…

cs.CR2025

HauntAttack: When Attack Follows Reasoning as a Shadow

Jingyuan Ma, Rui Li, Zheng Li +4

Emerging Large Reasoning Models (LRMs) consistently excel in mathematical and reasoning tasks, showcasing remarkable capabilities. However, the enhancement of reasoning abilities a…

cs.CL2025

Towards Harmonized Uncertainty Estimation for Large Language Models

Rui Li, Jing Long, Muge Qi +4

To facilitate robust and trustworthy deployment of large language models (LLMs), it is essential to quantify the reliability of their generations through uncertainty estimation. Wh…

cs.CL2025

Be a Multitude to Itself: A Prompt Evolution Framework for Red Teaming

Rui Li, Peiyi Wang, Jingyuan Ma +3

Large Language Models (LLMs) have gained increasing attention for their remarkable capacity, alongside concerns about safety arising from their potential to produce harmful content…

cs.CL2025

How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation

Rui Li, Heming Xia, Xinfeng Yuan +4

Recently, LLMs have garnered increasing attention across academic disciplines for their potential as human digital twins, virtual proxies designed to replicate individuals and auto…