Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y. HellaSwag: Can a machine really ﬁnish your sentence?
In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4791–4800,
Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1472. URL https:
//aclanthology.org/P19-1472.

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A Detailed results

A.1 Scaling laws

We evaluate models of different sizes on the downstream tasks described in Section 4.2. We show the results in Table
15.
Table 15: Results for several models trained on 1× 1022 FLOPs. This is with an architecture and data mixture that is
different from PaLM 2. When validation loss is roughly equal, downstream performance is approximately equal as well.
All evaluations are done in a 1-shot setting. Unless mentioned otherwise, accuracy is used as the evaluation metric. The
number of parameters indicates non-embedding parameters.
PaLM 2 Technical Report