超越生成式人工智能：自然语言生成的路线图
Beyond Generative Artificial Intelligence: Roadmap for Natural Language Generation
AI总结：
本文通过综述NLG领域近期代表性研究，提出研究路线图，旨在识别LLM尚未充分解决的NLG问题并指引未来研究方向。
摘要：
由于大型语言模型（LLMs）的出现，生成式人工智能呈指数级增长。这得益于自然语言处理（NLP）及其子领域自然语言生成（NLG）领域内所创建的深度学习方法的出色表现，而NLG正是本文关注的焦点。在不断壮大的LLM家族中，流行的GPT-4、Bard，尤其是像ChatGPT这样的工具，已成为其他LLM在解决NLG研究中大多数任务时的基准。这一现状引发了关于NLG下一步发展以及该领域如何适应和演变以应对LLM时代新挑战的新问题。为解决这一问题，本文对近期发表的NLG领域具有代表性的综述样本进行了回顾。通过这样做，我们旨在为科学界提供一份研究路线图，以识别LLM尚未充分解决的NLG方面，并建议未来应开展的研究方向。
英文摘要：
Generative Artificial Intelligence has grown exponentially as a result of Large Language Models (LLMs). This has been possible because of the impressive performance of deep learning methods created within the field of Natural Language Processing (NLP) and its subfield Natural Language Generation (NLG), which is the focus of this paper. Within the growing LLM family are the popular GPT-4, Bard and more specifically, tools such as ChatGPT have become a benchmark for other LLMs when solving most of the tasks involved in NLG research. This scenario poses new questions about the next steps for NLG and how the field can adapt and evolve to deal with new challenges in the era of LLMs. To address this, the present paper conducts a review of a representative sample of surveys recently published in NLG. By doing so, we aim to provide the scientific community with a research roadmap to identify which NLG aspects are still not suitably addressed by LLMs, as well as suggest future lines of research that should be addressed going forward.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
