Reported model / MODEL EVIDENCE

GLaM (64B/64E) (Zero-shot)

1 benchmarks · 1 observations · 1 sources

Scores at a glance

1 benchmarks · large dot: GLaM (64B/64E) (Zero-shot); small dots: closest models where reported

4853.2

GLaM (64B/64E) (Zero-shot) · ARC AI2 · Challenge score · % ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

How similarity is measured

Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.

Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

  1. GLaM: Efficient Scaling of Language Models with Mixture-of-Experts ↗

    Du, Nan, Huang, Yanping, Dai, Andrew M., Tong, Simon, Lepikhin, Dmitry, Xu, Yuanzhong et al. · 2021

    All 27 authors

    Du, Nan, Huang, Yanping, Dai, Andrew M., Tong, Simon, Lepikhin, Dmitry, Xu, Yuanzhong, Krikun, Maxim, Zhou, Yanqi, Yu, Adams Wei, Firat, Orhan, Zoph, Barret, Fedus, Liam, Bosma, Maarten, Zhou, Zongwei, Wang, Tao, Wang, Yu Emma, Webster, Kellie, Pellat, Marie, Robinson, Kevin, Meier-Hellstern, Kathleen, Duke, Toju, Dixon, Lucas, Zhang, Kun, Le, Quoc V, Wu, Yonghui, Chen, Zhifeng, Cui, Claire

Names used in source tables

GLaM (64B/64E) (Zero-shot)