7 papers
Instruction Tuning for Large Language Models: A Survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li +8
This paper surveys research works in the quickly advancing field of instruction tuning (IT), which can also be referred to as supervised fine-tuning (SFT)\footnote{In this paper, u…
FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset
Shuhe Wang, Xiaoya Li, Jiwei Li +8
Due to the data-driven nature of current face identity (FaceID) customization methods, all state-of-the-art models rely on large-scale datasets containing millions of high-quality…
Reinforcement Learning Enhanced LLMs: A Survey
Shuhe Wang, Shengyu Zhang, Jie Zhang +7
Reinforcement learning (RL) enhanced large language models (LLMs), particularly exemplified by DeepSeek-R1, have exhibited outstanding performance. Despite the effectiveness in imp…
Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment after Instruction Tuning
Guanlin Li, Kangjie Chen, Shangwei Guo +6
Large language models (LLMs) have emerged as powerful tools for addressing a wide range of general inquiries and tasks. Despite this, fine-tuning aligned LLMs on smaller, domain-sp…
Warfare:Breaking the Watermark Protection of AI-Generated Content
Guanlin Li, Yifei Chen, Jie Zhang +5
AI-Generated Content (AIGC) is rapidly expanding, with services using advanced generative models to create realistic images and fluent text. Regulating such content is crucial to p…
Turn That Frown Upside Down: FaceID Customization via Cross-Training Data
Shuhe Wang, Xiaoya Li, Xiaofei Sun +4
Existing face identity (FaceID) customization methods perform well but are limited to generating identical faces as the input, while in real-world applications, users often desire…