A Two-Stage Statistical Framework for Evaluating Associative Interference in Large Language Models

The article presents a two-stage statistical framework for evaluating associative interference in large language models (LLMs) using an adaptation of the Implicit Association Test (IAT). The study assesses three models—Claude Sonnet-4, Gemini 2.5 Pro, and GPT-5—finding that interference effects varied significantly, with Claude Sonnet-4 showing strong effects in specific domains, while GPT-5 exhibited minimal interference. This research emphasizes the need for model-specific evaluations of bias and suggests that modern LLMs can mitigate associative interference, which is crucial for practitioners focusing on ethical AI deployment.