@inproceedings{wang-etal-2026-future,
    title = "Future of Work in the Age of {LLM}s",
    author = "Wang, Zora  and
      Shao, Yijia  and
      Nguyen, David  and
      Yang, Diyi",
    editor = "Andreas, Jacob  and
      Murray, Kenton",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Tutorial Abstracts)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-1.1/",
    pages = "1--2",
    ISBN = "979-8-89176-394-4",
    abstract = "The recent development of large language models (LLMs) has revolutionized the landscape of human work. These models possess the ability to follow complex human instructions and operate versatile computer software, enabling them to participate in, augment, or even automate realistic occupational tasks that once thought to be exclusive to humans. As LLMs are increasingly integrated into workplaces, they are already reshaping labor dynamics (Hoffmann et al., 2024; Demirci et al., 2025) and raising urgent concerns about job displacement, diminished human agency, and overreliance on automation (Hazra et al., 2025). As a result, the future of work is undergoing a profound transformation: How will human occupations and task requirements evolve over time? And what roles will LLM-based systems play, as they become increasingly capable collaborators and autonomous workers? And how can we build technological and data infrastructures to support human-AI collaboration? This tutorial will provide an overview of the future of work shaped by the interplay of LLMs and humans, examining the emerging challenges, opportunities, and ethical considerations in this dynamic landscape. We begin by examining the economic landscape of work and how NLP technologies drive automation, followed by methods for developing LLMs that augment human labor and recent advances in LLM-based agents. We then cover evaluation approaches for workplace contexts, including datasets, benchmarks, and metrics, and conclude with open questions on technical, human, and societal implications."
}