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arXiv AI By Nirupam Chetlapalli, Yiming Liao, Min-Chun Chen, Keke Chen

Diverse by Reasoning: Harnessing the Wisdom of LLM Crowds for Future Prediction

Read the original on arXiv AI →

The paper introduces a behavior‑aware framework to build diverse crowds of large language models (LLMs) for future prediction. By analyzing reasoning traces on independent tasks, clustering models by behavioral similarity, and selecting representative medoids, the authors demonstrate that a small, well‑chosen crowd can outperform a larger, conventional voting ensemble. Experiments with 25 LLMs across multiple benchmarks show significant reductions in model calls and inference cost while improving prediction accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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