Feb. 12, 2024, 7 p.m. | 1littlecoder

1littlecoder www.youtube.com

We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method is orthogonal to existing complicated methods to further enhance LLMs, while the degree of enhancement is correlated to the task difficulty. We conduct comprehensive experiments on a wide range of LLM benchmarks to verify the presence of our finding, and to study the properties that can facilitate its occurrence.

🔗 Links 🔗
https://arxiv.org/abs/2402.05120

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agents language language models large language large language models llms performance sampling via voting

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

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@ Centre for the Governance of AI | Hybrid; Oxford, UK

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