arXiv:2605.30459v1 Announce Type: new Abstract: Large language models (LLMs) remain limited on tasks requiring indirect reasoning, cultural knowledge, and coordinated hypothesis testing. We investigate whether team-based interaction improves LLM performance in What? Where? When? (ChGK), a quiz game designed to reward collective reasoning. We introduce three team strategies: Voting, Silent Team (the captain observes final answers), and Talkative Team (the captain observes both answers and rationales). To minimize data leakage, we evaluate these strategies on a dataset consisting of 572 ChGK que
The rapid advancement and widespread deployment of large language models are prompting investigations into their limitations and potential for collective intelligence.
This research provides critical insights into methods for enhancing LLM capabilities beyond individual reasoning, particularly for complex tasks requiring coordination and nuanced understanding.
The understanding of how LLMs can overcome inherent limitations through team-based strategies is improving, pointing towards more sophisticated AI agentic designs.
- · AI developers
- · Research institutions
- · SaaS providers
- · Knowledge economy sectors
- · Solitary LLM system architectures
- · Basic prompt engineering
- · Monolithic AI development approaches
This directly demonstrates improved performance of LLMs when operating in coordinated team structures.
The findings could accelerate the development of complex, multi-agent AI systems capable of tackling more intricate real-world problems.
This might lead to new paradigms in human-AI collaboration where AI teams interact with human teams, fundamentally altering workflows.
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Read at arXiv cs.CL