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cs.CL2026

How AI Assistants Respond to Repeated Abuse

William Guey, Wei Zhang, Pierrick Bougault +4

AI assistants are expected to remain useful during difficult interactions, but little is known about how repeated verbal abuse changes their engagement with an otherwise benign tas…

cs.CL2026

Bias Audits Detect Bias but Disagree on Ranking: Evidence from Ten Instruments and Ten Frontier Models

William Guey, Pierrick Bougault, Wei Zhang +2

Emerging AI regulation mandates bias audits of high-risk systems, and audit scores are beginning to be used to rank models. Both uses assume different audit tools measure the same…

cs.CL2026

Same question, different history: language, national identity, and credit in large language models

William Guey, Pierrick Bougault, Wei Zhang +2

Who invented the radio, Russia's Alexander Popov or Italy's Guglielmo Marconi? Was the telephone the achievement of Bell in the United States or Meucci in Italy? Does printing belo…

cs.CL2026

Self-Preference Is Weak or Absent in Verifiable Instruction-Following Revision: A Four-Model Test Under Genuine Authorship

William Guey, Pierrick Bougault

Large language models (LLMs) increasingly review and revise text, including their own. A documented self-preference bias (models favoring their own generations when acting as judge…

cs.CL2026

Auditing demographic bias in AI-based emergency police dispatch: a cross-lingual evaluation of eleven large language models

William Guey, Wei Zhang, Pierrick Bougault +4

Large language models (LLMs) are rapidly being integrated into high-stakes public safety systems, including emergency call triage and dispatch decision support, yet their demograph…

cs.CL2026

BiasLab: A Multilingual Dual-Framing Framework for LLM Bias Measurement, Applied to Workplace and HR Contexts

William Guey, Wei Zhang, Pei-Luen Patrick Rau +4

Background: Large language models (LLMs) harbor systematic biases that are particularly consequential in workplace and HR contexts, where their outputs increasingly influence hirin…