Natural language generation (NLG) models have emerged as a focal point of research within natural language processing (NLP), exhibiting remarkable performance in tasks such as text composition and dialogue generation. However, their intricate architectures and extensive model parameters pose significant challenges to interpretability, limiting their applicability in high-stakes decision-making scenarios. To address this issue, human-computer interaction (HCI) and visualization techniques offer promising avenues to enhance the transparency and usability of NLG models by making their decision-making processes more interpretable. In this paper, we provide a comprehensive investigation into the roles, limitations, and impact of HCI and visualization in facilitating human understanding and control over NLG systems. We introduce a taxonomy of interaction methods and visualization techniques, categorizing three major research domains and their corresponding six key tasks in the application of NLG models. Finally, we summarize the shortcomings in the existing work and investigate the key challenges and emerging opportunities in the era of large language models (LLMs).

翻译：自然语言生成（NLG）模型已成为自然语言处理（NLP）领域的研究热点，在文本创作、对话生成等任务中展现出卓越性能。然而，其复杂的架构与庞大的模型参数对可解释性提出了重大挑战，限制了其在高风险决策场景中的应用。为解决这一问题，人机交互（HCI）与可视化技术为增强NLG模型的透明度和可用性提供了可行路径，通过使其决策过程更易于理解。本文系统探讨了HCI与可视化在促进人类理解和控制NLG系统方面的作用、局限性与影响。我们提出了交互方法与可视化技术的分类体系，归纳了NLG模型应用中的三大研究领域及其对应的六项关键任务。最后，我们总结了现有工作的不足，并探讨了大语言模型（LLM）时代面临的核心挑战与新兴机遇。