Smaller Language Models Are Better Instruction Evolvers
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}
6760eccda4962b8ccefc461e
Tingfeng Hui
claimed_verified
2024-12-17T08:03:52.567Z
66fa30dee6210a5175235a3c
/avatars/dff82a480fe61789bbb59285fb5c4ddb.svg
Tingfeng Hui
user
Chaox72
6760eccda4962b8ccefc461f
Lulu Zhao
6760eccda4962b8ccefc4620
Guanting Dong
admin_assigned
2024-12-17T09:34:16.450Z
61cd4b833dd34ba1985e0753
https://cdn-avatars.huggingface.co/v1/production/uploads/61cd4b833dd34ba1985e0753/BfHfrwotoMESpXZOHiIe4.png
KABI
user
dongguanting
6760eccda4962b8ccefc4621
Yaqi Zhang
6760eccda4962b8ccefc4622
Hua Zhou
6760eccda4962b8ccefc4623
Sen Su
Smaller Language Models Are Better Instruction Evolvers
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}
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.
instruction tuning large language models instruction evolution smaller language models instruction complexity IFD score IC-IFD