1 citations · 3 across the 23 of their papers we have counts for
4 papers · 1 filter
Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions
Yutao Hou, Yajing Luo, Zhiwen Ruan +4
Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, most benchmarks typic…
SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters
Yan Yang, Zeguan Xiao, Xin Lu +5
The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release…
SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation
Yixia Li, Boya Xiong, Guanhua Chen +1
Out-of-distribution (OOD) detection is crucial for the safe deployment of neural networks. Existing CLIP-based approaches perform OOD detection by devising novel scoring functions…
Distract Large Language Models for Automatic Jailbreak Attack
Zeguan Xiao, Yan Yang, Guanhua Chen +1
Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values. However, even meticulously aligned LLMs remai…