title: Instruction Following without Instruction Tuning
summary: 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.
paper_authors._id: 66f69c7519fa4bbc452507bb
paper_authors.name: John Hewitt
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T13:36:25.646Z
paper_authors._id: 646ec1161a198f8520f3c705
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/QPCRraySl9zTnf7eG5ZJk.png
paper_authors.fullname: John Hewitt
paper_authors.type: user
paper_authors.user: johnhew
paper_authors._id: 66f69c7519fa4bbc452507bc
paper_authors.name: Nelson F. Liu
paper_authors._id: 66f69c7519fa4bbc452507bd
paper_authors.name: Percy Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T13:35:42.185Z
paper_authors._id: 6409651b9e9f790c905b2335
paper_authors.avatarUrl: /avatars/1fb8c80b60f21f65a0a027319101f236.svg
paper_authors.fullname: Percy Liang
paper_authors.type: user
paper_authors.user: percyliang
paper_authors._id: 66f69c7519fa4bbc452507be
paper_authors.name: Christopher D. Manning
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T13:35:34.664Z
paper_authors._id: 5f555a1f8bf55658acfed19e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1628695407761-5f555a1f8bf55658acfed19e.jpeg
paper_authors.fullname: Christopher Manning
paper_authors.type: user
paper_authors.user: manning
paper_title: Instruction Following without Instruction Tuning
paper_summary: 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.
paper_ai_keywords: instruction tuning implicit instruction tuning instruction-response pairs instructional-style mapping narrow-domain data product-of-experts