title: Becoming self-instruct: introducing early stopping criteria for minimal
    instruct tuning
summary: In this paper, we introduce the Instruction Following Score (IFS), a metric
  that detects language models' ability to follow instructions. The metric has a
  dual purpose. First, IFS can be used to distinguish between base and instruct
  models. We benchmark publicly available base and instruct models, and show that
  the ratio of well formatted responses to partial and full sentences can be an
  effective measure between those two model classes. Secondly, the metric can be
  used as an early stopping criteria for instruct tuning. We compute IFS for
  Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models
  learn to follow instructions relatively early in the training process, and the
  further finetuning can result in changes in the underlying base model
  semantics. As an example of semantics change we show the objectivity of model
  predictions, as defined by an auxiliary metric ObjecQA. We show that in this
  particular case, semantic changes are the steepest when the IFS tends to
  plateau. We hope that decomposing instruct tuning into IFS and semantic factors
  starts a new trend in better controllable instruct tuning and opens
  possibilities for designing minimal instruct interfaces querying foundation
  models.
paper_authors._id: 64ab91993c974392fbbedec5
paper_authors.name: Waseem AlShikh
paper_authors.status: claimed_verified
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paper_authors.fullname: Waseem AlShikh
paper_authors.type: user
paper_authors.user: wassemgtk
paper_authors._id: 64ab91993c974392fbbedec6
paper_authors.name: Manhal Daaboul
paper_authors._id: 64ab91993c974392fbbedec7
paper_authors.name: Kirk Goddard
paper_authors._id: 64ab91993c974392fbbedec8
paper_authors.name: Brock Imel
paper_authors._id: 64ab91993c974392fbbedec9
paper_authors.name: Kiran Kamble
paper_authors.status: claimed_verified
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paper_authors.fullname: Kiran Kamble
paper_authors.type: user
paper_authors.user: kiranr
paper_authors._id: 64ab91993c974392fbbedeca
paper_authors.name: Parikshith Kulkarni
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/5cf841c7bb77bc83997061f1dadc4d87.svg
paper_authors.fullname: Parikshith Kulkarni
paper_authors.type: user
paper_authors.user: parikshithkulkarni
paper_authors._id: 64ab91993c974392fbbedecb
paper_authors.name: Melisa Russak
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-28T07:46:49.629Z
paper_authors._id: 60e61b3969bd0df25c9375da
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1625692968400-noauth.jpeg
paper_authors.fullname: Melisa Russak
paper_authors.type: user
paper_authors.user: melisa
paper_title: Becoming self-instruct: introducing early stopping criteria for minimal
    instruct tuning
paper_summary: In this paper, we introduce the Instruction Following Score (IFS), a metric
  that detects language models' ability to follow instructions. The metric has a
  dual purpose. First, IFS can be used to distinguish between base and instruct
  models. We benchmark publicly available base and instruct models, and show that
  the ratio of well formatted responses to partial and full sentences can be an
  effective measure between those two model classes. Secondly, the metric can be
  used as an early stopping criteria for instruct tuning. We compute IFS for
  Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models
  learn to follow instructions relatively early in the training process, and the
  further finetuning can result in changes in the underlying base model
  semantics. As an example of semantics change we show the objectivity of model
  predictions, as defined by an auxiliary metric ObjecQA. We show that in this
  particular case, semantic changes are the steepest when the IFS tends to
  plateau. We hope that decomposing instruct tuning into IFS and semantic factors
  starts a new trend in better controllable instruct tuning and opens
  possibilities for designing minimal instruct interfaces querying foundation
  models.
paper_ai_summary: The Instruction Following Score (IFS) is introduced to evaluate and control instruction-following ability in language models, aiding in distinguishing between base and instruct models as well as serving as an early stopping criterion in training.
paper_ai_keywords: Instruction Following Score (IFS) instruct models base models Supervised Fine-Tuning (SFT) LLaMA models semantic changes ObjecQA