@inproceedings{huang-etal-2025-enable,
title = "How to Enable Effective Cooperation Between Humans and {NLP} Models: A Survey of Principles, Formalizations, and Beyond",
author = "Huang, Chen and
Deng, Yang and
Lei, Wenqiang and
Lv, Jiancheng and
Chua, Tat-Seng and
Huang, Jimmy",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.22/",
doi = "10.18653/v1/2025.acl-long.22",
pages = "466--488",
ISBN = "979-8-89176-251-0",
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."
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%0 Conference Proceedings
%T How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond
%A Huang, Chen
%A Deng, Yang
%A Lei, Wenqiang
%A Lv, Jiancheng
%A Chua, Tat-Seng
%A Huang, Jimmy
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F huang-etal-2025-enable
%X 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.
%R 10.18653/v1/2025.acl-long.22
%U https://aclanthology.org/2025.acl-long.22/
%U https://doi.org/10.18653/v1/2025.acl-long.22
%P 466-488
Markdown (Informal)
[How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond](https://aclanthology.org/2025.acl-long.22/) (Huang et al., ACL 2025)
ACL
- Chen Huang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua, and Jimmy Huang. 2025. How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 466–488, Vienna, Austria. Association for Computational Linguistics.