Against The Achilles' Heel: A Survey on Red Teaming for Generative Models

no code implementations • 31 Mar 2024 • Lizhi Lin, Honglin Mu, Zenan Zhai, Minghan Wang, Yuxia Wang, Renxi Wang, Junjie Gao, Yixuan Zhang, Wanxiang Che, Timothy Baldwin, Xudong Han, Haonan Li

Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed.

Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities

1 code implementation • 17 Mar 2024 • Honglin Mu, Yang Xu, Yunlong Feng, Xiaofeng Han, Yitong Li, Yutai Hou, Wanxiang Che

With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably.

MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning

no code implementations • 19 Apr 2023 • Bohan Li, Longxu Dou, Yutai Hou, Yunlong Feng, Honglin Mu, Qingfu Zhu, Qinghua Sun, Wanxiang Che

Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template.

Data Augmentation Few-Shot Learning +1