Illusory Pattern Perception Drives Spurious Inference in Large Language Models
Read the original on arXiv AI →The paper "Illusory Pattern Perception Drives Spurious Inference in Large Language Models" reports that large language models (LLMs) exhibit a cognitive bias similar to humans’ tendency to see patterns where none exist. By adapting classic psychological experiments to three tasks, the authors find that LLMs often over‑associate frequent positive attributes with majority groups or large organizations and are prone to constructing causal narratives from ambiguous events. Using a Sparse Autoencoder‑based interpretability framework, the study links these illusory perceptions to holistic frequency perception and analytic cognitive orientation within the models.
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