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.

该论文对 iNLP 的定义、分类、评估方法、应用和未来研究方向进行了综合性调查，旨在为研究人员提供广泛的视野和工具，以促进与人类、知识库、模型和环境的交互、交流和合作。