Complementary LLM Selection Boosts Ensemble Performance
A new paper on arXiv (2605.24048) reframes proposer selection for LLM ensembles as a combinatorial problem akin to feature selection, emphasizing complementarity among models. Existing methods focus on accuracy or diversity but ignore interactions between proposers and the summarizer. The authors explore computationally feasible approaches to overcome the prohibitive time complexity of standard feature-selection algorithms in LLM settings.
Key facts
- Paper arXiv:2605.24048 proposes a new method for selecting LLMs in multi-AI collaboration.
- The approach treats proposer selection as a combinatorial selection problem.
- It emphasizes complementarity among LLMs rather than just accuracy or diversity.
- Standard feature-selection algorithms are impractical due to time complexity.
- The study explores computationally feasible alternatives.
- The paper is categorized as a cross-type abstract.
- The method aims to improve ensemble and debating pipelines.
- The work addresses interactions between proposers and the summarizer LLM.
Entities
Institutions
- arXiv