Law
Protecting Y our LLMs with Information Bottleneck
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content. Despite efforts to ethically align LLMs, these are often fragile and can be circumvented by jailbreaking attacks through optimized or manual adversarial prompts.
A Benchmark for Long-context Interleaved Video-Language Understanding Haoning Wu Dongxu Li Bei Chen Junnan Li Rhymes AI
Specifically, as part of the question, it contains a referring query that references related video contexts, called referred context .
Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models
Recently, various strategies for distributed training of large language models (LLMs) have been proposed.
Generative Forests
We use this now common parlance expression on purpose, to avoid confusion with the other "generative"
When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search Xuan Chen
Warning: This paper contains unfiltered and potentially harmful content.
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement Zhehao Huang, Xinwen Cheng, JingHao Zheng, Haoran Wang, Zhengbao He, Tao Li, Xiaolin Huang Shanghai Jiao Tong University
However, computing the second-order Hessian for large-scale models is intractable.
Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era
However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques.
