ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors

1 code implementation • 26 Feb 2024 • Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang

The safety of Large Language Models (LLMs) has gained increasing attention in recent years, but there still lacks a comprehensive approach for detecting safety issues within LLMs' responses in an aligned, customizable and explainable manner.

Rethinking Dense Retrieval's Few-Shot Ability

1 code implementation • 12 Apr 2023 • Si Sun, Yida Lu, Shi Yu, Xiangyang Li, Zhonghua Li, Zhao Cao, Zhiyuan Liu, Deiming Ye, Jie Bao

Moreover, the dataset is disjointed into base and novel classes, allowing DR models to be continuously trained on ample data from base classes and a few samples in novel classes.

Retrieval