GANPrompt: Improving LLM-Based Recommendations with GAN-Enhanced Diversity Prompts

Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language, with an increasing presence in the field of recommendation systems. However, LLMs still encounter a significant issue known as prompt sensitivity, where the model's output is susceptible to minor changes in the input prompt. This challenge is particularly problematic in recommendation systems, which rely on stable and consistent outputs. Fine-tuning LLMs with diverse prompts can reduce prompt sensitivity, but also lead to a decline in recommendation performance. Therefore, choosing an effective fine-tuning method is important to achieve enhanced robustness without sacrificing performance. To address prompt sensitivity while maintaining recommendation performance, we propose G enerative A dversarial N etwork-based p rompt enhancement (GANPrompt), a framework for improving LLM-based recommendation systems using adversarial game theory. In this framework, the generator and discriminator compete to produce diverse prompts, which are then used to fine-tune LLM-based recommendation systems, enhancing both robustness and accuracy. Specifically, to generate diverse prompts for fine-tuning and enhance the robustness of LLMs, we develop a GAN-based generator for diverse prompts, with an attribute generation module providing the foundational data support. Further, we introduce a diversity constraint to ensure that the generated prompts maintain high diversity while preserving semantic consistency. To maintain accuracy during the fine-tuning process, we introduce an explicit guidance knowledge token integration method. This method reduces noise and information loss in the face of diverse prompts by enhancing the use of traditional collaborative signals. Through extensive experiments on four publicly available datasets and one real-world industrial dataset, we demonstrate the effectiveness of the proposed framework. Our source code is available at https://github.com/LxytIUON/GANPrompt .