CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement
arXiv:2606.05793v1 Announce Type: new Abstract: While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging. Most of the existing conversation-level collaborative studies lack grounded interaction and behavioral execution, motivating the need for cooperative game environments that enable contextualized and immersive collaboration. To this end, this paper proposes CollabBench, a benchmark for evaluating and training collaborative agents in cooperative games. CollabBench features a Diverse Player Profile Simulation pipeline to model
The rapid advancement and adoption of LLMs necessitate a focus on their collaborative capabilities with human partners, moving beyond individual task proficiency.
Improving LLM collaboration is crucial for their integration into complex workflows, enabling sophisticated automation and enhancing human-AI team performance across various industries.
The development of a benchmark like CollabBench provides a structured way to evaluate and advance the collaborative intelligence of LLMs, accelerating their practical application.
- · AI developers
- · Enterprise software companies
- · Research institutions
- · Human-AI collaboration platforms
- · Companies reliant on siloed AI systems
- · Manual workflow providers
More capable LLM agents are developed, enhancing their utility in complex, interactive environments.
Automation expands into areas previously requiring intricate human coordination, leading to new service models and job roles.
The definition of 'work' evolves as human-AI teams become the norm, potentially shifting economic value creation and societal structures.
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Read at arXiv cs.CL