Instruction Following without Instruction Tuning
Authors: name: John Hewitt, name: Nelson F. Liu, name: Percy Liang, name: Christopher D. Manning

Instruction tuning commonly means finetuning a language model on
instruction-response pairs. We discover two forms of adaptation (tuning) that
are deficient compared to instruction tuning, yet still yield instruction
following; we call this implicit instruction tuning. We first find that
instruction-response pairs are not necessary: training solely on responses,
without any corresponding instructions, yields instruction following. This
suggests pretrained models have an instruction-response mapping which is
revealed by teaching the model the desired distribution of responses. However,
we then find it's not necessary to teach the desired distribution of responses:
instruction-response training on narrow-domain data like poetry still leads to
broad instruction-following behavior like recipe generation. In particular,
when instructions are very different from those in the narrow finetuning
domain, models' responses do not adhere to the style of the finetuning domain.
To begin to explain implicit instruction tuning, we hypothesize that very
simple changes to a language model's distribution yield instruction following.
We support this by hand-writing a rule-based language model which yields
instruction following in a product-of-experts with a pretrained model. The
rules are to slowly increase the probability of ending the sequence, penalize
repetition, and uniformly change 15 words' probabilities. In summary,
adaptations made without being designed to yield instruction following can do
so implicitly.