--- title: "Future of Work in the Age of LLMs — paper by Wang et al. (2026)" description: "It then covers ways to build LLMs that augment workers and recent LLM agents, followed by workplace-focused evaluation (datasets…" authors: ["Zora Wang", "Yijia Shao", "David Nguyen", "Diyi Yang"] year: 2026 venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)" source: "https://aclanthology.org/2026.acl-tutorials.1/" doi: "10.18653/v1/2026.acl-tutorials.1" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/wang-2026-future-work-age-llms/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2026-future-work-age-llms/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/31/) / Future of Work in the Age of LLMs # Future of Work in the Age of LLMs **Authors:** Zora Wang, [Yijia Shao](https://md.research.elamur.ai/free/en/authors/yijia-shao/), David Nguyen and [Diyi Yang](https://md.research.elamur.ai/free/en/authors/diyi-yang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.acl-tutorials.1/) · [PDF](https://aclanthology.org/2026.acl-tutorials.1.pdf) · [DOI 10.18653/v1/2026.acl-tutorials.1](https://doi.org/10.18653/v1/2026.acl-tutorials.1) · [OpenAlex](https://openalex.org/W7166867331) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary This is a two-page tutorial abstract, not an empirical paper, so it presents no new data, experiments, or results. Its subject is how large language models are reshaping human labor. LLMs can now carry out elaborate instructions and operate software, so they can support or even fully take over job tasks once seen as human-only. The authors note that this is already affecting labor markets and raising fears about lost jobs, weaker human control, and excessive dependence on automation. Three questions guide the tutorial: how occupations and their task demands will shift, what role LLM-based systems will play as partners or independent workers, and what technical and data infrastructure human–AI teamwork requires. The planned sequence starts with the economics of work and NLP-driven automation. It then covers ways to build LLMs that augment workers and recent LLM agents, followed by workplace-focused evaluation (datasets, benchmarks, metrics). It closes with open technical, human, and societal questions. Its main value is connecting NLP research with economic and human-centered views of work for the ACL audience. ## Key points - Venue: ACL 2026 Proceedings, Volume 5 (Tutorial Abstracts), pages 1–2. Materials are hosted at future-of-work-llm-tutorial.github.io. - Authors: Zora Wang (CMU, Language Technologies Institute), Yijia Shao and Diyi Yang (Stanford Computer Science), and David Nguyen (Stanford Digital Economy Lab at HAI; ESCoE). - The tutorial has five parts: (1) the economic landscape and automation, (2) LLMs that augment labor, (3) LLM-based agents, (4) evaluation in workplace settings, and (5) open questions. - The abstract names no specific datasets, benchmarks, or numerical findings. It cites Hoffmann et al. (2024) and Demirci et al. (2025) on labor effects, and Hazra et al. (2025) on risks such as overreliance. - The tutorial builds on Yang's earlier tutorials on language agents (EMNLP 2024), social intelligence (NAACL 2025), and human–AI collaboration (ACL 2025). *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (5): [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,772 works · [🤝 Safety Research](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · 543 works · [🤝 Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · 577 works · [📈 Economics, Econometrics and Finance](https://md.research.elamur.ai/free/en/shelves/economics-econometrics-and-finance/) · 17 works · [📈 Economics and Econometrics](https://md.research.elamur.ai/free/en/shelves/economics-econometrics-and-finance/economics-and-econometrics/) · 12 works 🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works ## Cite Cite as: Zora Wang, Yijia Shao, David Nguyen and Diyi Yang. 2026. Future of Work in the Age of LLMs. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{wang2026future, title = {{Future of Work in the Age of LLMs}}, author = {Zora Wang, Yijia Shao, David Nguyen and Diyi Yang}, booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)}, year = {2026}, doi = {10.18653/v1/2026.acl-tutorials.1}, url = {https://doi.org/10.18653/v1/2026.acl-tutorials.1} } ``` - [← previous FusionFlow: Enabling Deep Structural Exploration for Automated Agentic Workflow Generation](https://md.research.elamur.ai/free/en/papers/wang-2026-fusionflow-enabling-deep-structural-exploration/) - [next → G&P2P: A Multi-Source Approach to Grapheme-to-Phoneme Conversion](https://md.research.elamur.ai/free/en/papers/peng-2026-g-p2p-multi-source-approach/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/31/) *Updated 1 November 2026*