Researchers propose heterogeneity metric for LLM team selection
A new arXiv paper introduces a heterogeneity‑driven framework for selecting small teams of large language models (LLMs). The authors profile each model’s capability and extract two complementary signals: one that captures decorrelation in error patterns to avoid co‑failures, and another that measures divergence in predictive behavior to ensure strategic diversity.\n\nTeam composition is cast as…
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Key points
- Framework profiles LLMs for error decorrelation and predictive divergence to build heterogeneous teams
- Team selection formulated as a quality‑complementarity combinatorial objective solved by greedy search
- Benchmarks show heterogeneity‑driven teams outperform quality‑only baselines under controlled pools
Read the original at arXiv cs.CL · by Liangyu Teng, Hengsong Liu, Juncen Guo, Jingyu Zhang, Yang Liu, Jing Liu, Liang Song primary sourceOpen source ↗
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