title: Only-IF:Revealing the Decisive Effect of Instruction
    Diversity on Generalization
summary: Understanding and accurately following instructions is critical for large
  language models (LLMs) to be effective across diverse tasks. In this work, we
  rigorously examine the key factors that enable models to generalize to unseen
  instructions, providing insights to guide the collection of data for
  instruction-tuning. Through controlled experiments, inspired by the
  Turing-complete Markov algorithm, we demonstrate that such generalization
  only emerges when training data is diversified enough across
  semantic domains. Our findings also reveal that merely diversifying within
  limited domains fails to ensure robust generalization. In contrast,
  cross-domain data diversification, even under constrained data budgets,
  significantly enhances a model's adaptability. We further extend our analysis
  to real-world scenarios, including fine-tuning of
  $textbf{specialist} and textbf{generalist}$ models.
  In both cases, we demonstrate that 1) better performance can be achieved by
  increasing the diversity of an established dataset while keeping the data size
  constant, and 2) when scaling up the data, diversifying the semantics of
  instructions is more effective than simply increasing the quantity of similar
  data. Our research provides important insights for dataset collation,
  particularly when optimizing model performance by expanding training data for
  both specialist and generalist scenarios. We show that careful consideration of
  data diversification is key: training specialist models with data extending
  beyond their core domain leads to significant performance improvements, while
  generalist models benefit from diverse data mixtures that enhance their overall
  instruction-following capabilities across a wide range of applications. Our
  results highlight the critical role of strategic diversification and offer
  clear guidelines for improving data quality.
paper_authors._id: 6705e93f01c631bb8a88a9f7
paper_authors.name: Dylan Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-09T07:37:26.824Z
paper_authors._id: 642b8add48f67b6f21d4eb20
paper_authors.avatarUrl: /avatars/f15025b39248daa19a18e6ccb2eaaa0c.svg
paper_authors.fullname: Dylan
paper_authors.type: user
paper_authors.user: shizhuo2
paper_authors._id: 6705e93f01c631bb8a88a9f8
paper_authors.name: Justin Wang
paper_authors._id: 6705e93f01c631bb8a88a9f9
paper_authors.name: Francois Charton
paper_title: Only-IF:Revealing the Decisive Effect of Instruction
    Diversity on Generalization
paper_summary: Understanding and accurately following instructions is critical for large
  language models (LLMs) to be effective across diverse tasks. In this work, we
  rigorously examine the key factors that enable models to generalize to unseen
  instructions, providing insights to guide the collection of data for
  instruction-tuning. Through controlled experiments, inspired by the
  Turing-complete Markov algorithm, we demonstrate that such generalization
  only emerges when training data is diversified enough across
  semantic domains. Our findings also reveal that merely diversifying within
  limited domains fails to ensure robust generalization. In contrast,
  cross-domain data diversification, even under constrained data budgets,
  significantly enhances a model's adaptability. We further extend our analysis
  to real-world scenarios, including fine-tuning of
  $textbf{specialist} and textbf{generalist}$ models.
  In both cases, we demonstrate that 1) better performance can be achieved by
  increasing the diversity of an established dataset while keeping the data size
  constant, and 2) when scaling up the data, diversifying the semantics of
  instructions is more effective than simply increasing the quantity of similar
  data. Our research provides important insights for dataset collation,
  particularly when optimizing model performance by expanding training data for
  both specialist and generalist scenarios. We show that careful consideration of
  data diversification is key: training specialist models with data extending
  beyond their core domain leads to significant performance improvements, while
  generalist models benefit from diverse data mixtures that enhance their overall
  instruction-following capabilities across a wide range of applications. Our
  results highlight the critical role of strategic diversification and offer
  clear guidelines for improving data quality.
paper_ai_summary: Data diversification across semantic domains significantly enhances instruction-following capabilities in LLMs, leading to better generalization and performance.
paper_ai_keywords: generalization instruction-tuning Turing-complete Markov algorithm semantic domains cross-domain data diversification specialist models generalist models data diversification instruction-following capabilities