  In the realm of large language models (LLMs), enhancing instruction-following
capability often involves curating expansive training data. This is achieved
through two primary schemes: i) Scaling-Inputs: Amplifying (input, output)
pairs per task instruction, aiming for better instruction adherence. ii)
Scaling Input-Free Tasks: Enlarging tasks, each composed of an (instruction,
output) pair (without requiring a separate input anymore). However, LLMs under
Scaling-Inputs tend to be overly sensitive to inputs, leading to
misinterpretation or non-compliance with instructions. Conversely, Scaling
Input-Free Tasks demands a substantial number of tasks but is less effective in
instruction following when dealing with instances in Scaling-Inputs. This work
introduces MUFFIN, a new scheme of instruction-following dataset curation.
Specifically, we automatically Scale Tasks per Input by diversifying these
tasks with various input facets. Experimental results across four zero-shot
benchmarks, spanning both Scaling-Inputs and Scaling Input-Free Tasks schemes,
reveal that LLMs, at various scales, trained on MUFFIN generally demonstrate
superior instruction-following capabilities compared to those trained on the
two aforementioned schemes.
