Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks

What happened

The article presents a multi-task behavioral audit of Chinese LLMs, demonstrating that task design and ironic language (e.g., yinyang register) alter bias measurement outcomes. It also evaluates three task forms (social consensus estimation, high-stakes decision, controlled association rating) and finds discrepancies in model performance across these formats.

Why it matters

For AI developers and content governance professionals, the study highlights the need to account for linguistic nuances and task design when evaluating LLMs for sensitive applications like toxic content detection.

• Task design directly influences bias measurement in LLMs.
• Irony detection remains a critical challenge for content governance systems.
• Model evaluations should include multiple task forms and linguistic registers.

The bigger picture

The findings suggest that bias evaluation frameworks for LLMs must incorporate diverse linguistic registers and task designs to ensure robust safety assessments.

The study underscores the importance of rigorous, context-aware evaluation methods for Chinese LLMs to address bias and improve reliability in content governance applications.

Technical details

• Yinyang register: A form of echoic irony involving contradictory or paradoxical expressions.
• Task forms: Form A (social consensus estimation), Form B (high-stakes decision), Form C (controlled association rating).
• Error rate metrics: Classification error rates increased from 10.9% (formal register) to 23.5% (yinyang register).
• Inter-rater agreement: Measured via majority-vote kappa (0.299) for yinyang register.

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