Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence. This paradigm considers language models as agents capable of observing, acting, and receiving feedback iteratively from external entities. Specifically, language models in this context can: (1) interact with humans for better understanding and addressing user needs, personalizing responses, aligning with human values, and improving the overall user experience; (2) interact with knowledge bases for enriching language representations with factual knowledge, enhancing the contextual relevance of responses, and dynamically leveraging external information to generate more accurate and informed responses; (3) interact with models and tools for effectively decomposing and addressing complex tasks, leveraging specialized expertise for specific subtasks, and fostering the simulation of social behaviors; and (4) interact with environments for learning grounded representations of language, and effectively tackling embodied tasks such as reasoning, planning, and decision-making in response to environmental observations. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept. We then provide a systematic classification of iNLP, dissecting its various components, including interactive objects, interaction interfaces, and interaction methods. We proceed to delve into the evaluation methodologies used in the field, explore its diverse applications, scrutinize its ethical and safety issues, and discuss prospective research directions. This survey serves as an entry point for researchers who are interested in this rapidly evolving area and offers a broad view of the current landscape and future trajectory of iNLP.

翻译：交互式自然语言处理（iNLP）已成为自然语言处理领域的一种新范式，旨在解决现有框架中的局限性，同时契合人工智能的最终目标。该范式将语言模型视为能够从外部实体迭代观察、行动和接收反馈的智能体。具体而言，在此背景下，语言模型能够：（1）与人类交互，以更好理解和满足用户需求、个性化回应、对齐人类价值观、改善整体用户体验；（2）与知识库交互，通过事实知识丰富语言表征、提升回应的上下文相关性、动态利用外部信息生成更准确且信息丰富的回应；（3）与模型和工具交互，有效分解和处理复杂任务、利用特定子任务的专长、促进社会行为的模拟；（4）与环境交互，学习语言的有形表征，并有效应对具身任务，如基于环境观察进行推理、规划和决策。本文对iNLP进行了全面综述，首先提出了该概念的统一定义和框架。随后，我们对iNLP进行系统分类，剖析其各个组成部分，包括交互对象、交互界面和交互方法。接着，深入探讨该领域的评估方法，探索其多样化应用，审视其伦理与安全问题，并讨论未来研究方向。本综述为对该快速发展领域感兴趣的研究人员提供了切入点，并全面展现了iNLP的当前格局与未来轨迹。