A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a response, which may result in homogenization of the responses, as well as certain demographic groups being under-represented or even erased from the generated responses. In this paper, we formalize diversity of representation in generative LLMs. We present evaluation datasets and propose metrics to measure diversity in generated responses along people and culture axes. We find that LLMs understand the notion of diversity, and that they can reason and critique their own responses for that goal. This finding motivated a new prompting technique called collective-critique and self-voting (CCSV) to self-improve people diversity of LLMs by tapping into its diversity reasoning capabilities, without relying on handcrafted examples or prompt tuning. Extensive empirical experiments with both human and automated evaluations show that our proposed approach is effective at improving people and culture diversity, and outperforms all baseline methods by a large margin.

翻译：生成式大语言模型（LLMs）面临一个关键挑战：多样性。当用户提示词未明确指定时，模型可能在生成回复时遵循隐含假设，导致回复同质化，特定人口群体在生成内容中被低估甚至被抹除。本文对生成式大语言模型的表征多样性进行了形式化定义，提出评估数据集并设计沿人物与文化维度的多样性量化指标。研究发现，LLMs具备对多样性概念的理解能力，并能为达成该目标进行自我推理与回复评判。这一发现催生了名为"集体评判与自投票"（CCSV）的新型提示工程技术——通过激活模型内在的多样性推理能力实现自我改进，无需依赖人工构建示例或提示调优。通过人类评估与自动化评估的大规模实验证明，该方法能有效提升人物与文化多样性，并以显著优势超越所有基线方法。