In this article, we propose a framework for enhancing black-box text-driven generators to produce factual, explicit, and high-quality synthesis. Our approach leverages the power of external knowledge bases, such as Wikipedia, to strengthen these models and improve their performance.
Firstly, we discuss the challenges of using black-box text-driven generators and how they can lead to suboptimal results. We then present our proposed framework, which consists of three main components: Parsing Demonstration, Enhancement Request, and Knowledge Rejection. These components allow for more control over the generator’s output and help ensure that it produces factual and explicit content.
We also explore the use of knowledge context visualization to further improve the quality of the generated text. This involves retrieving informative references and evidence from external knowledge bases, such as Wikipedia, to provide context and support for the generator’s outputs.
Our proposed framework enables different textual styles of the enhanced prompts, allowing for more flexibility in how the generator is instructed. We demonstrate the effectiveness of our approach through experiments on several benchmark datasets.
Overall, our work aims to provide a more accessible and reliable way to generate high-quality text using black-box generators, making it easier for users to create informative and engaging content without relying solely on trial and error. By leveraging the power of external knowledge bases and enhancing the generator’s performance, our approach has the potential to revolutionize the field of natural language processing.
