哪个职业更好？
我正在报名参加课程，目前在自然语言处理课程和大型语言模型（LLM）工程课程之间犹豫。哪个选项更好呢？我感觉最近关于LLM在不久的将来可能变得无关紧要的讨论很多。
自然语言处理：介绍人类语言的计算建模；持续努力创建能够用自然语言与人类交流的计算机程序；以及自然语言领域的当前应用，例如自动文档分类、智能查询处理和信息提取。课程主题包括语法的计算模型和自动解析、统计语言模型和大规模文本语料库的分析、自然语言语义以及理解语言的程序、话语结构模型和智能代理的语言使用。课程作业包括对语言模型的形式和数学分析，以及实现能够分析和解释自然语言文本的工作程序。具备统计学知识会有所帮助。
LLM集成系统工程：研究集成大型语言模型（LLM）的系统的软件工程基础。考察LLM集成系统如何将自然语言指令转化为行动。提供构建具有自然流畅界面的系统的机会，将其与现有软件集成，严格测试其行为，并理解其失败模式和局限性。
我不确定哪一个对我更有帮助！作为背景，我是数据科学专业，但对未来从事机器学习工作很感兴趣！
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Signing up for classes and I am debating between a Natural Language Processing class and a LLM engineering class. Which one is the better option? I feel like there’s been a lot of recent discourse about LLMs becoming irrelevant in the near future.<p>Natural Language Processing: Introduces the computational modeling of human language; the ongoing effort to create computer programs that can communicate with people in natural language; and current applications of the natural language field, such as automated document classification, intelligent query processing, and information extraction. Topics include computational models of grammar and automatic parsing, statistical language models and the analysis of large text corpora, natural language semantics and programs that understand language, models of discourse structure, and language use by intelligent agents. Course work includes formal and mathematical analysis of language models and implementation of working programs that analyze and interpret natural language text. Knowledge of statistics is helpful.<p>Engineering LLM-Integrated Systems: Studies the software engineering foundations for systems that integrate large language models (LLMs). Examines how LLM-integrated systems turn natural language instructions into actions. Offers opportunities to build systems with natural and fluid interfaces, integrate them with existing software, rigorously test their behavior, and understand their failure modes and limitations.<p>Not sure which one will be more helpful! For context I am a data science major but interested in working in machine learning in the future!