作者位次 #6/7ACL 2026Findings· Findings of ACL
C 2 PO : Diagnosing and Disentangling Bias Shortcuts in LLM s
Xuan Feng · Bo An · Tianlong Gu · Liang Chang · Fengrui Hao · Peipeng Yu · Shuai Zhao
摘要
Bias in Large Language Models (LLMs) poses significant risks to trustworthiness, manifesting primarily as stereotypical biases (e.g., gender or racial stereotypes) and structural biases (e.g., lexical overlap or position preferences). However, prior paradigms typically address these in isolation,...