10 citations · 32 across the 17 of their papers we have counts for
17 papers
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming
Suyu Ge, Chunting Zhou, Rui Hou +5
Red-teaming is a common practice for mitigating unsafe behaviors in Large Language Models (LLMs), which involves thoroughly assessing LLMs to identify potential flaws and addressin…
Improving Input-label Mapping with Demonstration Replay for In-context Learning
Zhuocheng Gong, Jiahao Liu, Qifan Wang +4
In-context learning (ICL) is an emerging capability of large autoregressive language models where a few input-label demonstrations are appended to the input to enhance the model's…
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
Jiduan Liu, Jiahao Liu, Qifan Wang +5
Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these mode…
Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems
Tianyuan Shi, Liangzhi Li, Zijian Lin +3
Efficient knowledge retrieval plays a pivotal role in ensuring the success of end-to-end task-oriented dialogue systems by facilitating the selection of relevant information necess…
Attack Prompt Generation for Red Teaming and Defending Large Language Models
Boyi Deng, Wenjie Wang, Fuli Feng +3
Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or au…
Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication
Jianli Bai, Xiangfu Song, Xiaowu Zhang +4
A private decision tree evaluation (PDTE) protocol allows a feature vector owner (FO) to classify its data using a tree model from a model owner (MO) and only reveals an inference…