Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2025
Abstract
With the advancement of large language models (LLMs), intelligent models have evolved from mere tools to autonomous agents with their own goals and strategies for cooperating with humans. This evolution has birthed a novel paradigm in NLP, i.e., human-model cooperation, that has yielded remarkable progress in numerous NLP tasks in recent years. In this paper, we take the first step to present a thorough review of human-model cooperation, exploring its principles, formalizations, and open challenges. In particular, we introduce a new taxonomy that provides a unified perspective to summarize existing approaches. Also, we discuss potential frontier areas and their corresponding challenges. We regard our work as an entry point, paving the way for more breakthrough research in this regard.
Discipline
Artificial Intelligence and Robotics | Programming Languages and Compilers
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria, 2025 July 27 - August 1
First Page
466
Last Page
488
Identifier
10.18653/v1/2025.acl-long.22
Publisher
Association for Computational Linguistics
City or Country
USA
Citation
HUANG, Chen; DENG, Yang; LEI, Wenqiang; LV, Jiancheng; CHUA, Tat-Seng; and HUANG, Jimmy.
How to enable effective cooperation between humans and NLP models: A survey of principles, formalizations, and beyond. (2025). Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria, 2025 July 27 - August 1. 466-488.
Available at: https://ink.library.smu.edu.sg/sis_research/10373
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Programming Languages and Compilers Commons