Only-IF:Revealing the Decisive Effect of Instruction
  Diversity on Generalization
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.
6705e93f01c631bb8a88a9f7
Dylan Zhang
claimed_verified
2024-10-09T07:37:26.824Z
642b8add48f67b6f21d4eb20
/avatars/f15025b39248daa19a18e6ccb2eaaa0c.svg
Dylan
user
shizhuo2
6705e93f01c631bb8a88a9f8
Justin Wang
6705e93f01c631bb8a88a9f9
Francois Charton
Only-IF:Revealing the Decisive Effect of Instruction
  Diversity on Generalization
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.
Data diversification across semantic domains significantly enhances instruction-following capabilities in LLMs, leading to better generalization and performance.
generalization instruction-tuning Turing-complete Markov algorithm semantic domains cross-domain data diversification specialist models generalist models data diversification instruction-following capabilities