# Current and Future Use of Large Language Models for Knowledge Work

arXiv: https://arxiv.org/abs/2503.16774 · HTML: https://mlsanity.com/paper/2503.16774/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2025.

## Built on by (most cited first, 10 of 10)

- [Evaluating Long-Horizon Memory for Multi-Party Collaborative Dialogues](https://mlsanity.com/paper/2602.01313/) (2026)
- [Glia: A Human-Inspired AI for Automated Systems Design and Optimization](https://mlsanity.com/paper/2510.27176/) (2025)
- [Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study](https://mlsanity.com/paper/2506.13464/) (2025)
- [Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA](https://mlsanity.com/paper/2603.14782/) (2026)
- [CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases](https://mlsanity.com/paper/2608.27391/) (2026)
- [BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications](https://mlsanity.com/paper/2509.24908/) (2025)
- [Impact of LLMs on Team Collaboration in Software Development](https://mlsanity.com/paper/2510.08612/) (2025)
- [Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations](https://mlsanity.com/paper/2604.22109/) (2026)
- [Probabilistic Concept-Aware Steering for Trustworthy LLM Inference](https://mlsanity.com/paper/2607.18259/) (2026)
- [Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agents](https://mlsanity.com/paper/2609.11660/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have introduced a paradigm shift in interaction with AI technology, enabling knowledge workers to complete tasks by specifying their desired outcome in natural language. LLMs have the potential to increase productivity and reduce tedious tasks in an unprecedented way. A systematic study of LLM adoption for work can provide insight into how LLMs can best support these workers. To explore knowledge workers' current and desired usage of LLMs, we ran a survey (n=216). Workers described tasks they already used LLMs for, like generating code or improving text, but imagined a future with LLMs integrated into their workflows and data. We ran a second survey (n=107) a year later that validated our initial findings and provides insight into up-to-date LLM use by knowledge workers. We discuss implications for adoption and design of generative AI technologies for knowledge work.