A Language Model is a term that encompasses various types of models designed
to understand and generate human communication. Large Language Models (LLMs)
have gained significant attention due to their ability to process text with
human-like fluency and coherence, making them valuable for a wide range of
data-related tasks fashioned as pipelines. The capabilities of LLMs in natural
language understanding and generation, combined with their scalability,
versatility, and state-of-the-art performance, enable innovative applications
across various AI-related fields, including eXplainable Artificial Intelligence
(XAI), Automated Machine Learning (AutoML), and Knowledge Graphs (KG).
Furthermore, we believe these models can extract valuable insights and make
data-driven decisions at scale, a practice commonly referred to as Big Data
Analytics (BDA). In this position paper, we provide some discussions in the
direction of unlocking synergies among these technologies, which can lead to
more powerful and intelligent AI solutions, driving improvements in data
pipelines across a wide range of applications and domains integrating humans,
computers, and knowledge.

语言模型是一种广义的术语，它包含了各种类型的模型，旨在理解和生成人类的交流。大型语言模型（LLM）因其具有与人类类似的流畅和连贯性处理文本的能力而引起了人们的广泛关注，这使它们在以管道方式构建的各种数据相关任务中具有价值。LLM 在自然语言理解和生成方面的能力，结合其可伸缩性、多样性和领先性能，使其在诸如解释性人工智能（XAI）、自动化机器学习（AutoML）和知识图谱（KG）等各个人工智能领域具有创新应用的能力。此外，我们还相信这些模型能够从大规模数据中提取有价值的见解，并进行数据驱动的决策，这种做法通常被称为大数据分析（BDA）。在本立场论文中，我们对这些技术之间的协同作用提供一些讨论，该协同作用可以实现更强大和智能的人工智能解决方案，推动在整合人、计算机和知识的各种应用和领域中数据管道的改进。