Language of Thought Shapes Output Diversity in Large Language Models

ArXi:2601.11227v2 Announce Type: replace Output diversity is crucial for Large Language Models as it underpins pluralism and creativity. In this work, we reveal that controlling the language used during model thinking-the language of thought-provides a novel and structural source of output diversity. Our preliminary study shows that different thinking languages occupy distinct regions in a model's thinking space.