The emergence of LLMs, like ChatGPT and Gemini, has marked the modern era of artificial intelligence applications characterized by high-impact applications generating text, images, and videos. However, these models usually ensue with one critical challenge called hallucination: confident presentation of inaccurate or fabricated information. This problem attracts serious concern when these models are applied to specialized domains, including healthcare and law, where the accuracy and preciseness of information are absolute conditions. In this paper, we propose EvoLLMs, an innovative framework inspired by Evolutionary Computation, which automates the generation of high-quality Question-answering (QA) datasets while minimizing hallucinations. EvoLLMs employs genetic algorithms, mimicking evolutionary processes like selection, variation, and mutation, to guide LLMs in generating accurate, contextually relevant question-answer pairs. Comparative analysis shows that EvoLLMs consistently outperforms human-generated datasets in key metrics such as Depth, Relevance, and Coverage, while nearly matching human performance in mitigating hallucinations. These results highlight EvoLLMs as a robust and efficient solution for QA dataset generation, significantly reducing the time and resources required for manual curation.

翻译：以ChatGPT和Gemini为代表的大语言模型（LLMs）的出现，标志着人工智能应用进入了现代时代，其特点是能够生成文本、图像和视频等高影响力应用。然而，这些模型通常伴有一个称为“幻觉”的关键挑战：即对不准确或捏造的信息进行自信的呈现。当这些模型应用于医疗保健和法律等专业领域时，这个问题引起了严重关切，因为在这些领域中，信息的准确性和精确性是绝对条件。在本文中，我们提出了EvoLLMs，一个受进化计算启发的创新框架，它能够自动生成高质量的问答（QA）数据集，同时最大限度地减少幻觉。EvoLLMs采用遗传算法，模拟选择、变异和突变等进化过程，来引导LLMs生成准确且与上下文相关的问答对。对比分析表明，EvoLLMs在深度、相关性和覆盖率等关键指标上持续优于人工生成的数据集，同时在缓解幻觉方面几乎达到了人类水平。这些结果突显了EvoLLMs作为一种稳健高效的QA数据集生成解决方案，显著减少了人工整理所需的时间和资源。