While Large Language Models (LLMs) have made significant strides in replicating human-like abilities, there are concerns about a reduction in the linguistic diversity of their outputs. This results in the homogenization of viewpoints and perspectives, as well as the underrepresentation of specific demographic groups. Although several fine-tuning and prompting techniques have been suggested to tackle the issue, they are often tailored to specific tasks or come with a substantial increase in computational cost and latency. This makes them challenging to apply to applications that demand very low latency, such as chatbots and virtual assistants. We propose Possibility Exploration Fine-Tuning (PEFT), a task-agnostic framework that enhances the text diversity of LLMs without increasing latency or computational cost. Given the same prompt, models fine-tuned with PEFT can simultaneously generate multiple diverse responses, each corresponding with a controllable possibility number. Experiments on dialogue and story generation tasks demonstrate that PEFT significantly enhances the diversity of LLM outputs, as evidenced by lower similarity between candidate responses. Since PEFT emphasizes semantic diversity over lexical diversity, it can also notably reduce demographic bias in dialogue systems. The implementations and datasets are available in our repository: https://github.com/mailong25/peft_diversity

翻译：尽管大语言模型在复现类人能力方面取得了显著进展，但其输出语言多样性的降低引发了广泛担忧。这导致了观点与视角的同质化，以及特定人口群体的代表性不足。虽然已有多种微调与提示技术被提出以应对此问题，但这些方法通常针对特定任务定制，或伴随着计算成本与延迟的大幅增加，使其难以应用于聊天机器人和虚拟助手等要求极低延迟的场景。我们提出可能性探索微调（PEFT），这是一个与任务无关的框架，可在不增加延迟或计算成本的前提下提升大语言模型的文本多样性。给定相同提示时，经PEFT微调的模型能够同时生成多个多样化响应，每个响应对应一个可控的可能性编号。在对话与故事生成任务上的实验表明，PEFT能显著增强大语言模型输出的多样性，候选响应间较低的相似度证明了这一点。由于PEFT更注重语义多样性而非词汇多样性，它还能显著降低对话系统中的人口统计偏差。实现代码与数据集已发布于我们的代码库：https://github.com/mailong25/peft_diversity