Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·19h ago
AI Research
arXiv CS.AI·May 26·5 min read
Artificial Effort
TL;DR
Real-effort tasks in experimental economics assume human performance, but this study investigates their validity with AI. Using 23 Large Language Models (LLMs) on 8 tasks, it finds that most tasks can be automated effectively.
✦ Why It Matters
Engineers and researchers should reconsider the implications of AI on experimental economics and task validity.
Key Takeaways
- Evaluate the use of LLMs in your real-effort task designs.
- Understand that LLMs can perform many cognitive tasks at low cost.
- Reassess the validity of results from tasks completed with LLM assistance.
- Consider the implications of automation on experimental economics.
How It Works
The study utilized 23 LLMs to perform eight real-effort tasks, measuring their accuracy and cost-effectiveness. As LLMs evolved, their ability to handle these tasks improved significantly, indicating a trend towards greater automation in cognitive tasks.