A Two-Stage Statistical Framework for Evaluating Associative Interference in Large Language Models
arXiv:2606.14117v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly evaluated for bias using adaptations of human psychological paradigms, yet methodological limitations-particularly the conflation of refusal behavior with task performance-have hindered clear interpretation. Here, we adapt the Implicit Association Test (IAT) to a controlled, forced-choice framework and introduce a two-stage modeling approach that separates response compliance from task-consistent classification. Across three contemporary LLMs (Claude Sonnet-4, Gemini 2.5 Pro, and GPT-5), we evaluate
The increasing sophistication and widespread deployment of large language models necessitate more robust and nuanced evaluation methods for bias, moving beyond simplistic performance metrics.
Accurate bias evaluation is crucial for responsible AI development, mitigating discriminatory outcomes, and building public trust in advanced AI systems.
This framework introduces a more granular and reliable method to assess associative interference in LLMs, distinguishing between refusal behavior and actual task-consistent classification.
- · AI ethicists
- · Responsible AI developers
- · LLM evaluators
- · Organizations deploying LLMs
- · Developers ignoring bias evaluation
- · LLMs with unmitigated biases
- · Simple, undifferentiated bias evaluation methods
Improved understanding of LLM biases and their underlying mechanisms.
Development of more effective bias mitigation strategies and less discriminatory LLMs.
Increased regulatory scrutiny and industry standards for AI bias evaluation, leading to certification requirements.
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Read at arXiv cs.AI