as 14.9% absolute improvement on one-shot TyDiQA (Clark et al., 2020) and 8.1% on arithmetic reasoning
in under-represented languages (Shi et al., 2022). In human rater evaluations, Flan-PaLM substantially
outperforms PaLM on a challenging set of open-ended generation questions, suggesting improved usability.
Moreover, we found that instruction ﬁnetuning also improves performance across several responsible AI
evaluation benchmarks.
In addition, we also instruction-ﬁnetune Flan-T5 models (80M to 11B). These checkpoints have strong zero-
shot, few-shot, and CoT abilities, outperforming prior public checkpoints such as T5 (Raﬀel et al., 2020). For
example, Flan-T5 11B outperforms T5 11B by double-digit improvements and even outperforms PaLM 62B on
some challenging BIG-Bench tasks (Srivastava et al., 2022). Overall, our results underscore how instruction
ﬁnetuning can improve performance across a range of models, prompting setups, and evaluation tasks.
Scaling Instruction-Finetuned Language Models