Design and Report Benchmarks for Knowledge Work
The paper discusses the need for improved benchmarks in knowledge work AI, particularly in areas like coding and healthcare. It proposes a three-step approach to better align benchmark tasks with real-world work activities. The authors provide case analyses to illustrate how benchmark design influences the validity of performance claims.
- ▪The development of LLM agents has increased interest in knowledge-work AI.
- ▪Current evaluation methods for knowledge work often rely on traditional NLP task logic.
- ▪The paper introduces a three-step approach for designing and reporting benchmarks that reflect actual work activities.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.23262 (cs) [Submitted on 22 May 2026] Title:Design and Report Benchmarks for Knowledge Work Authors:Yining Hua, Hongbin Na, Cyrus Ayubcha, Levi Lian View a PDF of the paper titled Design and Report Benchmarks for Knowledge Work, by Yining Hua and 3 other authors View PDF HTML (experimental) Abstract:The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowledge-work evaluation and benchmark design still largely follow the logic of traditional NLP tasks. As a result, higher benchmark performance does not reliably show that a system can carry out knowledge work in real-world deployment settings.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
