HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation
📰 ArXiv cs.AI
Learn to evaluate LLM-based human-agent systems with configurable human participation using HAS-Bench
Action Steps
- Build a graph-based framework to represent humans and LLM-powered agents
- Configure human participation in the framework to evaluate different scenarios
- Evaluate the performance of LLM-based human-agent systems using HAS-Bench
- Analyze the results to identify areas for improvement in human-agent collaboration
- Apply the insights to refine the LLM-powered agents and human participation strategies
Who Needs to Know This
AI researchers and engineers working on human-agent systems can benefit from this framework to evaluate and improve their models
Key Insight
💡 HAS-Bench provides a configurable framework to evaluate human-agent systems, enabling more effective collaboration between humans and LLM-powered agents
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🤖 Evaluate LLM-based human-agent systems with HAS-Bench! 📊
Key Takeaways
Learn to evaluate LLM-based human-agent systems with configurable human participation using HAS-Bench
Full Article
Title: HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation
Abstract:
arXiv:2607.04329v1 Announce Type: new Abstract: Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework that represents humans and LLM-powered agents as first-class participants with explicit roles, permissions, communication paths, and action authority. Building on this framework, HAS-Bench evaluates Human-Agent Systems under configurable human participation across agency
Abstract:
arXiv:2607.04329v1 Announce Type: new Abstract: Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework that represents humans and LLM-powered agents as first-class participants with explicit roles, permissions, communication paths, and action authority. Building on this framework, HAS-Bench evaluates Human-Agent Systems under configurable human participation across agency
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