title: Smaller Language Models Are Better Instruction Evolvers
summary: Instruction tuning has been widely used to unleash the complete potential of
  large language models. Notably, complex and diverse instructions are of
  significant importance as they can effectively align models with various
  downstream tasks. However, current approaches to constructing large-scale
  instructions predominantly favour powerful models such as GPT-4 or those with
  over 70 billion parameters, under the empirical presumption that such larger
  language models (LLMs) inherently possess enhanced capabilities. In this study,
  we question this prevalent assumption and conduct an in-depth exploration into
  the potential of smaller language models (SLMs) in the context of instruction
  evolution. Extensive experiments across three scenarios of instruction
  evolution reveal that smaller language models (SLMs) can synthesize more
  effective instructions than LLMs. Further analysis demonstrates that SLMs
  possess a broader output space during instruction evolution, resulting in more
  complex and diverse variants. We also observe that the existing metrics fail to
  focus on the impact of the instructions. Thus, we propose Instruction
  Complex-Aware IFD (IC-IFD), which introduces instruction complexity in the
  original IFD score to evaluate the effectiveness of instruction data more
  accurately. Our source code is available at:
  https://github.com/HypherX/Evolution-Analysis{https://github.com/HypherX/Evolution-Analysis}
paper_authors._id: 6760eccda4962b8ccefc461e
paper_authors.name: Tingfeng Hui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-17T08:03:52.567Z
paper_authors._id: 66fa30dee6210a5175235a3c
paper_authors.avatarUrl: /avatars/dff82a480fe61789bbb59285fb5c4ddb.svg
paper_authors.fullname: Tingfeng Hui
paper_authors.type: user
paper_authors.user: Chaox72
paper_authors._id: 6760eccda4962b8ccefc461f
paper_authors.name: Lulu Zhao
paper_authors._id: 6760eccda4962b8ccefc4620
paper_authors.name: Guanting Dong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-17T09:34:16.450Z
paper_authors._id: 61cd4b833dd34ba1985e0753
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61cd4b833dd34ba1985e0753/BfHfrwotoMESpXZOHiIe4.png
paper_authors.fullname: KABI
paper_authors.type: user
paper_authors.user: dongguanting
paper_authors._id: 6760eccda4962b8ccefc4621
paper_authors.name: Yaqi Zhang
paper_authors._id: 6760eccda4962b8ccefc4622
paper_authors.name: Hua Zhou
paper_authors._id: 6760eccda4962b8ccefc4623
paper_authors.name: Sen Su
paper_title: Smaller Language Models Are Better Instruction Evolvers
paper_summary: Instruction tuning has been widely used to unleash the complete potential of
  large language models. Notably, complex and diverse instructions are of
  significant importance as they can effectively align models with various
  downstream tasks. However, current approaches to constructing large-scale
  instructions predominantly favour powerful models such as GPT-4 or those with
  over 70 billion parameters, under the empirical presumption that such larger
  language models (LLMs) inherently possess enhanced capabilities. In this study,
  we question this prevalent assumption and conduct an in-depth exploration into
  the potential of smaller language models (SLMs) in the context of instruction
  evolution. Extensive experiments across three scenarios of instruction
  evolution reveal that smaller language models (SLMs) can synthesize more
  effective instructions than LLMs. Further analysis demonstrates that SLMs
  possess a broader output space during instruction evolution, resulting in more
  complex and diverse variants. We also observe that the existing metrics fail to
  focus on the impact of the instructions. Thus, we propose Instruction
  Complex-Aware IFD (IC-IFD), which introduces instruction complexity in the
  original IFD score to evaluate the effectiveness of instruction data more
  accurately. Our source code is available at:
  https://github.com/HypherX/Evolution-Analysis{https://github.com/HypherX/Evolution-Analysis}
paper_ai_summary: Smaller language models can generate more effective and diverse instructions than larger models, challenging the assumption that model size directly correlates with instruction synthesis quality.
paper_ai_keywords: instruction tuning large language models instruction evolution smaller language models instruction complexity IFD score IC-IFD