objective alone does not know when to stop generating, and can make mistakes such as continuing the user’s
input rather than responding to it. In Section 6, we saw that on a set of open-ended evaluations, outputs from
Flan-PaLM had substantially better human ratings compared to outputs from PaLM, especially for CoT tasks
like complex reasoning, planning, and explanation. Flan-PaLM outperforms PaLM on several Responsible
AI benchmarks, particularly benchmarks measuring toxic language harms. These results are consistent with
ﬁndings from InstructGPT (Ouyang et al., 2022), which showed that ﬁnetuned models produce outputs that
are better aligned with human preferences. The zero-shot usability of models is important for wider adoption
of language models that do not require prompt engineering or require few-shot exemplars. A model card
(Mitchell et al., 2019) is included in the appendix.
Instruction ﬁnetuning is relatively compute-eﬃcient. Although scaling the size of language models has
Scaling Instruction-Finetuned Language Models