Repetitions are not all alike: distinct mechanisms sustain repetition in language models

Text generated by language models (LMs) can degrade into repetitive cycles, where identical word sequences are persistently repeated one after another. Prior research has typically treated repetition as a unitary phenomenon. However, repetitive sequences emerge under diverse tasks and contexts, raising the possibility that it may be driven by multiple underlying factors. Here, we experimentally explore the hypothesis that repetition in LMs can result from distinct mechanisms, reflecting different text generation strategies used by the model. We examine the internal working of LMs under two conditions that prompt repetition: one in which repeated sequences emerge naturally after human-written text, and another where repetition is explicitly induced through an in-context learning (ICL) setup. Our analysis reveals key differences between the two conditions: the model exhibits varying levels of confidence, relies on different attention heads, and shows distinct pattens of change in response to controlled perturbations. These findings suggest that distinct internal mechanisms can interact to drive repetition, with implications for its interpretation and mitigation strategies. More broadly, our results highlight that the same surface behavior in LMs may be sustained by different underlying processes, acting independently or in combination.