In recent years, Large Language Models (LLMs) have demonstrated remarkable proficiency in comprehending and generating natural language, with a growing prevalence in the domain of recommendation systems. However, LLMs still face a significant challenge called prompt sensitivity, which refers to that it is highly susceptible to the influence of prompt words. This inconsistency in response to minor alterations in prompt input may compromise the accuracy and resilience of recommendation models. To address this issue, this paper proposes GANPrompt, a multi-dimensional LLMs prompt diversity framework based on Generative Adversarial Networks (GANs). The framework enhances the model's adaptability and stability to diverse prompts by integrating GANs generation techniques with the deep semantic understanding capabilities of LLMs. GANPrompt first trains a generator capable of producing diverse prompts by analysing multidimensional user behavioural data. These diverse prompts are then used to train the LLMs to improve its performance in the face of unseen prompts. Furthermore, to ensure a high degree of diversity and relevance of the prompts, this study introduces a mathematical theory-based diversity constraint mechanism that optimises the generated prompts to ensure that they are not only superficially distinct, but also semantically cover a wide range of user intentions. Through extensive experiments on multiple datasets, we demonstrate the effectiveness of the proposed framework, especially in improving the adaptability and robustness of recommendation systems in complex and dynamic environments. The experimental results demonstrate that GANPrompt yields substantial enhancements in accuracy and robustness relative to existing state-of-the-art methodologies.

翻译：近年来，大型语言模型（LLMs）在自然语言理解与生成方面展现出卓越能力，在推荐系统领域的应用日益广泛。然而，LLMs仍面临一个重大挑战——提示敏感性，即模型极易受到提示词的影响。这种对提示输入微小变化产生的不一致响应，可能损害推荐模型的准确性与鲁棒性。为应对此问题，本文提出GANPrompt，一种基于生成对抗网络（GANs）的多维度LLMs提示多样性增强框架。该框架通过将GANs生成技术与LLMs的深度语义理解能力相结合，提升模型对多样化提示的适应性与稳定性。GANPrompt首先通过分析多维用户行为数据，训练能够生成多样化提示的生成器。这些多样化提示随后用于训练LLMs，以提升其在面对未见提示时的性能。此外，为确保提示的高度多样性与相关性，本研究引入基于数学理论的多样性约束机制，对生成提示进行优化，确保其不仅在表层形式上具有差异性，更能在语义层面覆盖广泛的用户意图。通过在多个数据集上的大量实验，我们验证了所提框架的有效性，特别是在提升推荐系统在复杂动态环境中的适应性与鲁棒性方面。实验结果表明，相较于现有先进方法，GANPrompt在准确性与鲁棒性上均取得显著提升。