English(EN) Using Poly-Encoders for Computationally Efficient Automated Creativity Assessment

多编码器提供高效的自动化创造力评估

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一种使用多编码器的自动化创造力评估新方法，与传统的语言大模型相比，该方法显著降低了计算需求。通过在约18,000个科学创造性思维测试的人工评分响应数据集上微调多编码器，该方法取得了与大型模型相当的性能。这种利用较小BERT编码器的方法，与人工评分者达到了高达r = 0.74的皮尔逊相关系数，使得在消费级硬件上进行可扩展的创造力评估成为可能。 AI

影响实现了更易于访问和可扩展的自动化创造力评估，可能影响教育和其他评估环境。

排序理由该条目是一篇学术论文，详细介绍了自动化创造力评估的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Sam Grouchnikov, Phillip Gregory, Jiho Noh · 2026-08-28 04:00

使用多编码器进行计算高效的自动化创造力评估

arXiv:2608.26165v1 Announce Type: cross Abstract: Automated creativity assessment has been a long standing challenge, with traditional methods often being resource intensive or lacking practical accuracy. We introduce a novel approach by using Poly-Encoder for computationally eff…

本文如何被排名

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使用多编码器进行计算高效的自动化创造力评估

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