can follow natural language instructions (Mishra
et al., 2022; Wei et al., 2022; Sanh et al., 2022;
Wang et al., 2022; Ouyang et al., 2022; Chung et al.,
2022, i.a.). These developments are powered by
two key components: large pre-trained language
models (LM) and human-written instruction data.
PROMPTSOURCE (Bach et al., 2022) and SUPER-
NATURALINSTRUCTIONS (Wang et al., 2022) are
two notable recent datasets that use extensive man-
ual annotation for collecting instructions to con-
struct T0 (Bach et al., 2022; Sanh et al., 2022) and
T𝑘-INSTRUCT (Wang et al., 2022). However, this
process is costly and often suﬀers limited diver-
sity given that most human generations tend to be
popular NLP tasks, falling short of covering a true
variety of tasks and diﬀerent ways to describe them.
Given these limitations, continuing to improve the
quality of instruction-tuned models necessitates the
development of alternative approaches for supervis-
ing instruction-tuned models.
SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions