6 citations · 26 across the 36 of their papers we have counts for
10 papers · 1 filter
Efficient LLM Adversarial Training via Low-Rank Defense and Circuit-Guided Surrogates
Weiyi He, Yuping Lin, Jiliang Tang +1
Adversarial training is one of the most effective defenses against adversarial attacks, yet the computational cost remains prohibitive at modern scales, especially for large langua…
A Simple Plug-in for Improving Eviction-Based KV Cache Compression
Yuping Lin, Jiayuan Ding, Yue Xing +3
KV cache growth is a major bottleneck for long-context inference in large language models. Existing methods are often dominated by binary eviction or representation approximation,…
Crafting Reversible SFT Behaviors in Large Language Models
Yuping Lin, Pengfei He, Yue Xing +5
Supervised fine-tuning (SFT) induces new behaviors in large language models, yet imposes no structural constraint on how these behaviors are distributed within the model. Existing…
SoK: Machine Unlearning for Large Language Models
Jie Ren, Yue Xing, Yingqian Cui +2
Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining t…
A General Framework to Enhance Fine-tuning-based LLM Unlearning
Jie Ren, Zhenwei Dai, Xianfeng Tang +7
Unlearning has been proposed to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based methods, wh…
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
Shenglai Zeng, Jiankun Zhang, Bingheng Li +8
Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively…