Benchmarking LLM Agents on Office Tasks with Economic Grounding.
Key takeaways
- A new benchmark, OmegaUse-OfficeVal, evaluates LLM agents on office tasks with economic metrics.
- Current LLM agents are cheaper and faster than humans for office tasks but lack human-level quality.
- The benchmark provides economic signals like human labor time and task price for value-weighted evaluation.
- Open-sourcing the code and dataset facilitates further research and development in AI office automation.
Who benefits
Summary
Researchers introduce OmegaUse-OfficeVal, a new benchmark for evaluating LLM agents on complex office-suite tasks, incorporating human labor time and task price to assess economic viability. The benchmark reveals current LLMs are faster and cheaper than humans but lack comparable quality.
Why it matters
Professionals can use this benchmark to understand the current capabilities and economic trade-offs of deploying LLM agents for office automation, guiding strategic decisions on AI adoption.
How to implement this in your domain
- 1Review the OmegaUse-OfficeVal benchmark tasks to identify potential automation opportunities within your organization.
- 2Pilot LLM agents on specific, well-defined office tasks, comparing their performance and cost against human baselines.
- 3Utilize the economic grounding metrics from the benchmark to calculate potential ROI for AI-driven automation initiatives.
- 4Integrate quality verification steps into LLM agent workflows, acknowledging current limitations in deliverable quality.
Original post by Jingbo Zhou, Yusai Zhao, Qi Bao, Jingjia Cao, Zhenghai Chen, Chang Gao, Kaiqi Guo, Muxin Guo, Mingxuan Li, Xinjiang Lu, Yanru Ma, Yixiong Xiao, Zenghui Zhang, Le Zhang, Hua Wu
"arXiv:2607.27155v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to assist users in completing tasks. However, existing benchmarks provide limited support for evaluating whether agents can carry out office-suite workflows at a reasonable…"
View on XPrimary sources
Originally posted by Jingbo Zhou, Yusai Zhao, Qi Bao, Jingjia Cao, Zhenghai Chen, Chang Gao, Kaiqi Guo, Muxin Guo, Mingxuan Li, Xinjiang Lu, Yanru Ma, Yixiong Xiao, Zenghui Zhang, Le Zhang, Hua Wu on X · view source
Want to go deeper?
Turn these trends into skills with Learnijoy's hands-on AI & tech courses.
Explore coursesMore in AI Research
New Framework Improves Partial Multi-View Clustering Performance.
DAS-PMVC is a novel framework for partial multi-view clustering that addresses view asymmetry and irrelevant samples by leveraging dual alignment and structure enhancement. It uses anchor graph structure alignment, structure-enhanced feature learning, and a dual alignment strategy to achieve superior clustering performance on various datasets.
Dual Teachers Improve Adversarial Robustness and Accuracy.
This work extends Information Bottleneck Distillation (IBD) by introducing a "clean teacher" alongside a robust teacher to improve the robustness/accuracy tradeoff against adversarial attacks. The proposed method transfers features from both teachers to a student model, achieving better clean accuracy while maintaining adversarial robustness, outperforming original IBD and competing with state-of-the-art approaches.
Dynamic Batch Sizes Improve Large Language Model Training Efficiency.
This paper proposes a new approach to deep learning dynamics, deriving joint scaling laws for loss based on both learning rate and batch size schedules. It introduces an optimal dynamic batch size schedule that consistently outperforms static batch size baselines, highlighting its importance for large language model training.