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Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refine
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论文简述:在这篇名为Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models的论文中,作者们探讨了开源语言模型(LLM)在成本性能权衡方面的民主化问题。他们注意到,目前市场上主要依赖于专有LLM服务,这导致了访问受限和信息隐私担忧的问题。为了应对这一挑战,作者提出了一种名为自我反思的开源模型迭代改进方法,以及一种新的评估指标——性能、精炼和推理成本得分(PeRFICS)来为特定任务选择最佳模型。实验结果显示,针对不同大小的开源模型(从7B到65B),其平均性能提高了8.2%。尤为引人注意的是,即使模型的内存占用非常小(如Vicuna-7B),整体性能也提高了11.74%,在高创造力和开放性任务方面甚至达到了25.39%的提升。Vicuna-13B进一步在Vicuna基准测试中超越了ChatGPT。这一工作对于资源受限和关注信息隐私的环境具有重要意义,它们可以在不产生高昂成本、牺牲性能和隐私的情况下利用LLM。通过域无关的自我反思过程以及新的评估指标PeRFICS,论文为模型选择提供了明智的决策支持,从而降低了成本和实现了LLM的民主化。 论文链接: https://arxiv.org/pdf/2310.07611
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