Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·21h ago
AI Research
arXiv CS.AI·Jun 10·7 min read
How can we assess human-agent interactions? Case studies in software agent design
TL;DR
Existing benchmarks for evaluating large language model (LLM) agents often overlook the collaborative nature of human-agent interactions. To address this, a new framework called PULSE was developed, which combines user feedback with machine learning to assess agent designs.
✦ Why It Matters
Engineers can leverage PULSE to enhance the design and evaluation of human-agent interactions in their applications.
Key Takeaways
- Adopt the PULSE framework for better human-agent evaluation.
- Utilize user feedback to inform agent design decisions.
- Expect discrepancies between benchmarks and real-world performance.
- Focus on reducing confidence intervals in user satisfaction assessments.
How It Works
PULSE operates by collecting user feedback on agent interactions, which is then used to train a machine learning model that predicts user satisfaction. This model's predictions are combined with actual user ratings to create a more nuanced understanding of agent performance, allowing for more informed design decisions.