title: Data Valuation using Neural Networks for Efficient Instruction
    Fine-Tuning
summary: Influence functions provide crucial insights into model training, but
  existing methods suffer from large computational costs and limited
  generalization. Particularly, recent works have proposed various metrics and
  algorithms to calculate the influence of data using language models, which do
  not scale well with large models and datasets. This is because of the expensive
  forward and backward passes required for computation, substantial memory
  requirements to store large models, and poor generalization of influence
  estimates to new data. In this paper, we explore the use of small neural
  networks -- which we refer to as the InfluenceNetwork -- to estimate influence
  values, achieving up to 99% cost reduction. Our evaluation demonstrates that
  influence values can be estimated with models just 0.0027% the size of full
  language models (we use 7B and 8B versions). We apply our algorithm of
  estimating influence values (called NN-CIFT: Neural Networks for effiCient
  Instruction Fine-Tuning) to the downstream task of subset selection for general
  instruction fine-tuning. In our study, we include four state-of-the-art
  influence functions and show no compromise in performance, despite large
  speedups, between NN-CIFT and the original influence functions. We provide an
  in-depth hyperparameter analyses of NN-CIFT. The code for our method can be
  found here: https://github.com/agarwalishika/NN-CIFT.
paper_authors._id: 67b4cb6c777b7676c8b3c43d
paper_authors.name: Ishika Agarwal
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-18T18:06:42.786Z
paper_authors._id: 6391e4e984afa726d66180b9
paper_authors.avatarUrl: /avatars/e437e2820745b522a868b8da27d9a11f.svg
paper_authors.fullname: Ishika Agarwal
paper_authors.type: user
paper_authors.user: ishikaa
paper_authors._id: 67b4cb6c777b7676c8b3c43e
paper_authors.name: Dilek Hakkani-Tür
paper_title: Data Valuation using Neural Networks for Efficient Instruction
    Fine-Tuning
paper_summary: Influence functions provide crucial insights into model training, but
  existing methods suffer from large computational costs and limited
  generalization. Particularly, recent works have proposed various metrics and
  algorithms to calculate the influence of data using language models, which do
  not scale well with large models and datasets. This is because of the expensive
  forward and backward passes required for computation, substantial memory
  requirements to store large models, and poor generalization of influence
  estimates to new data. In this paper, we explore the use of small neural
  networks -- which we refer to as the InfluenceNetwork -- to estimate influence
  values, achieving up to 99% cost reduction. Our evaluation demonstrates that
  influence values can be estimated with models just 0.0027% the size of full
  language models (we use 7B and 8B versions). We apply our algorithm of
  estimating influence values (called NN-CIFT: Neural Networks for effiCient
  Instruction Fine-Tuning) to the downstream task of subset selection for general
  instruction fine-tuning. In our study, we include four state-of-the-art
  influence functions and show no compromise in performance, despite large
  speedups, between NN-CIFT and the original influence functions. We provide an
  in-depth hyperparameter analyses of NN-CIFT. The code for our method can be
  found here: https://github.com/agarwalishika/NN-CIFT.
paper_ai_keywords: influence functions forward and backward passes multimodal representation InfluenceNetwork NN-CIFT: Neural Networks for effiCient Instruction Fine-Tuning hyperparameter analyses