思维语言塑造大型语言模型输出的多样性

摘要

输出多样性对大语言模型至关重要，它支撑着多元性与创造力。本研究发现，通过控制模型思考时使用的语言——即思维语言——能够为输出多样性提供新颖且结构化的来源。初步研究表明，不同思维语言在模型的思维空间中占据不同区域。基于此发现，我们研究了多语言思维下的两种重复采样策略：单语言采样与混合语言采样，并对所有输出（无论采用何种思维语言）统一控制为英语进行多样性评估。大量实验表明，将思维语言从英语切换至非英语语言能持续提升输出多样性，且存在清晰稳定的正相关关系：思维空间中与英语距离越远的语言带来的增益越大。我们进一步证明，通过组合效应聚合多思维语言的采样可产生额外提升，且基于语言异质性的规模采样能拓展模型的多样性上限。最后，这些发现在多元对齐场景中展现出实际价值，使大语言模型输出能覆盖更广泛的文化知识与价值取向。相关代码已开源：https://github.com/iNLP-Lab/Multilingual-LoT-Diversity。

English

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. Based on this observation, we study two repeated sampling strategies under multilingual thinking-Single-Language Sampling and Mixed-Language Sampling-and conduct diversity evaluation on outputs that are controlled to be in English, regardless of the thinking language used. Across extensive experiments, we demonstrate that switching the thinking language from English to non-English languages consistently increases output diversity, with a clear and consistent positive correlation such that languages farther from English in the thinking space yield larger gains. We further show that aggregating samples across multiple thinking languages yields additional improvements through compositional effects, and that scaling sampling with linguistic heterogeneity expands the model's diversity ceiling. Finally, we show that these findings translate into practical benefits in pluralistic alignment scenarios, leading to broader coverage of cultural knowledge and value orientations in LLM outputs. Our code is publicly available at https://github.com/iNLP-Lab/Multilingual-LoT-Diversity.

思维语言塑造大型语言模型输出的多样性

Language of Thought Shapes Output Diversity in Large Language Models

摘要

Support