GANPrompt: 利用GAN增强的多样性提示提升基于大语言模型的推荐系统鲁棒性
GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts
AI总结：
针对大语言模型推荐系统的提示敏感性问题，提出基于GAN的多维提示多样性框架GANPrompt，通过生成多样化提示训练模型并引入多样性约束，有效提升推荐的准确性与鲁棒性。
摘要：
近年来，大语言模型（Large Language Models, LLMs）在理解和生成自然语言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仍面临一项重大挑战——提示敏感性，即模型极易受到提示词的影响。提示输入的微小改动就会导致输出不一致，这可能会损害推荐模型的准确性与韧性。为解决这一问题，本文提出GANPrompt，一种基于生成对抗网络（Generative Adversarial Networks, GANs）的多维LLMs提示多样性框架。该框架将GANs生成技术与LLMs的深度语义理解能力相结合，增强模型对多样化提示的适应性与稳定性。GANPrompt首先通过分析多维度用户行为数据，训练一个能够生成多样化提示的生成器；随后利用这些多样化提示训练LLMs，以提升其面对未见过的提示时的性能。此外，为确保提示具备高度多样性与相关性，本研究引入了一种基于数学理论的多样性约束机制，对生成的提示进行优化，确保它们不仅在表面上存在差异，还能在语义上覆盖广泛的用户意图。通过在多个数据集上开展的大量实验，我们验证了所提框架的有效性，尤其体现在提升复杂动态环境下推荐系统的适应性与鲁棒性方面。实验结果表明，与现有的最优方法相比，GANPrompt在准确性和鲁棒性上均实现了显著提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
