用对比学习提升大模型问答生成质量，减少幻觉。
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models
- 引入对比学习与思维链提示，联合挖掘领域知识。
- 结合对比示例与思维链提示时，生成问题质量最高。
- 适合需要高质量问答数据的智能系统研发者。
随着人工智能技术的快速发展，特别是问答系统应用日益广泛，高质量的问题生成已成为支撑系统发展的关键环节。本文聚焦于基于知识的问题生成技术，旨在使计算机能够根据特定文本或知识库模拟人类提问过程。针对大模型在知识密集型任务中存在幻觉和知识缺失的问题，本文提出一种融合对比学习的增强型问题生成方法。该方法利用多个模型协同挖掘领域知识，并通过对比学习引导模型降低生成过程中的噪声与幻觉。实验结果表明，设计包含对比示例的提示后，模型在问题生成上的表现显著提升，尤其当同时使用对比指令与示例时，生成问题的质量达到最优，准确率也得到改善。结果证明，结合对比上下文与思维链提示的方法能有效提升问题生成的质量与实用性。
原文摘要 · Abstract (English)
With the rapid development of artificial intelligence technology, especially the increasingly widespread application of question-and-answer systems, high-quality question generation has become a key component in supporting the development of these systems. This article focuses on knowledge-based question generation technology, which aims to enable computers to simulate the human questioning process based on understanding specific texts or knowledge bases. In light of the issues of hallucination and knowledge gaps present in large-scale language models when applied to knowledge-intensive tasks, this paper proposes an enhanced question generation method that incorporates contrastive learning. This method utilizes multiple models to jointly mine domain knowledge and uses contrastive learning to guide the model in reducing noise and hallucinations in generation. Experimental results show that by designing prompts containing contrasting examples, the model's performance in question generation improves considerably, particularly when contrasting instructions and examples are used simultaneously, leading to the highest quality of generated questions and improved accuracy. These results demonstrate that the method proposed in this study, which combines contrasting context and chain-of-thought prompts, can effectively improve both the quality and the practicality of question generation.
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