15 citations · 17 across the 7 of their papers we have counts for
5 papers · 1 filter
Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting
Jinhu Fu, Yan Bai, Longzhu He +4
Large language models (LLMs) can effectively handle outdated information through knowledge editing. However, current approaches face two key limitations: (I) Poor generalization: M…
MindScope: Exploring cognitive biases in large language models through Multi-Agent Systems
Zhentao Xie, Jiabao Zhao, Yilei Wang +4
Detecting cognitive biases in large language models (LLMs) is a fascinating task that aims to probe the existing cognitive biases within these models. Current methods for detecting…
FairMonitor: A Dual-framework for Detecting Stereotypes and Biases in Large Language Models
Yanhong Bai, Jiabao Zhao, Jinxin Shi +3
Detecting stereotypes and biases in Large Language Models (LLMs) is crucial for enhancing fairness and reducing adverse impacts on individuals or groups when these models are appli…
A Survey of Explainable Knowledge Tracing
Yanhong Bai, Jiabao Zhao, Tingjiang Wei +2
With the long term accumulation of high quality educational data, artificial intelligence has shown excellent performance in knowledge tracing. However, due to the lack of interpre…
FairMonitor: A Four-Stage Automatic Framework for Detecting Stereotypes and Biases in Large Language Models
Yanhong Bai, Jiabao Zhao, Jinxin Shi +3
Detecting stereotypes and biases in Large Language Models (LLMs) can enhance fairness and reduce adverse impacts on individuals or groups when these LLMs are applied. However, the…