Leveraging Open Knowledge for Advancing Task Expertise in Large Language
  Models
The cultivation of expertise for large language models (LLMs) to solve tasks
of specific areas often requires special-purpose tuning with calibrated
behaviors on the expected stable outputs. To avoid huge cost brought by manual
preparation of instruction datasets and training resources up to hundreds of
hours, the exploitation of open knowledge including a wealth of low rank
adaptation (LoRA) models and instruction datasets serves as a good starting
point. However, existing methods on model and data selection focus on the
performance of general-purpose capabilities while neglecting the knowledge gap
exposed in domain-specific deployment. In the present study, we propose to
bridge such gap by introducing few human-annotated samples (i.e., K-shot) for
advancing task expertise of LLMs with open knowledge. Specifically, we develop
an efficient and scalable pipeline to cost-efficiently produce task experts
where K-shot data intervene in selecting the most promising expert candidates
and the task-relevant instructions. A mixture-of-expert (MoE) system is built
to make the best use of individual-yet-complementary knowledge between multiple
experts. We unveil the two keys to the success of a MoE system, 1) the abidance
by K-shot, and 2) the insistence on diversity. For the former, we ensure that
models that truly possess problem-solving abilities on K-shot are selected
rather than those blind guessers. Besides, during data selection, instructions
that share task-relevant contexts with K-shot are prioritized. For the latter,
we highlight the diversity of constituting experts and that of the fine-tuning
instructions throughout the model and data selection process. Extensive
experimental results confirm the superiority of our approach over existing
methods on utilization of open knowledge across various tasks. Codes and models
will be released later.
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Yuncheng Yang
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Yulei Qin
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Yulei Qin
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Tong Wu
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Zihan Xu
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Gang Li
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Gang Li
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Pengcheng Guo
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Pengcheng Guo
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pcguo
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Hang Shao
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Yucheng Shi
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Yucheng SHi
user
YuchengShi
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Ke Li
claimed_verified
2025-01-09T10:11:41.474Z
63280915eeee4dd858083092
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Ke Li
user
tristanli
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Xing Sun
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Jie Yang
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Yun Gu
extracted_pending
2025-05-08T02:24:36.692Z
681c15cb4e8877d583db8656
/avatars/517f4c88a714a1fbea394decb253045b.svg
yun
user
geron762
Leveraging Open Knowledge for Advancing Task Expertise in Large Language
  Models
The cultivation of expertise for large language models (LLMs) to solve tasks
of specific areas often requires special-purpose tuning with calibrated
behaviors on the expected stable outputs. To avoid huge cost brought by manual
preparation of instruction datasets and training resources up to hundreds of
hours, the exploitation of open knowledge including a wealth of low rank
adaptation (LoRA) models and instruction datasets serves as a good starting
point. However, existing methods on model and data selection focus on the
performance of general-purpose capabilities while neglecting the knowledge gap
exposed in domain-specific deployment. In the present study, we propose to
bridge such gap by introducing few human-annotated samples (i.e., K-shot) for
advancing task expertise of LLMs with open knowledge. Specifically, we develop
an efficient and scalable pipeline to cost-efficiently produce task experts
where K-shot data intervene in selecting the most promising expert candidates
and the task-relevant instructions. A mixture-of-expert (MoE) system is built
to make the best use of individual-yet-complementary knowledge between multiple
experts. We unveil the two keys to the success of a MoE system, 1) the abidance
by K-shot, and 2) the insistence on diversity. For the former, we ensure that
models that truly possess problem-solving abilities on K-shot are selected
rather than those blind guessers. Besides, during data selection, instructions
that share task-relevant contexts with K-shot are prioritized. For the latter,
we highlight the diversity of constituting experts and that of the fine-tuning
instructions throughout the model and data selection process. Extensive
experimental results confirm the superiority of our approach over existing
methods on utilization of open knowledge across various tasks. Codes and models
will be released later.
An efficient pipeline using few human-annotated samples and a mixture-of-experts system enhances the domain-specific capabilities of large language models with open knowledge.
large language models (LLMs) low rank adaptation (LoRA) few-shot learning (K-shot) mixture-of-experts (MoE) task expertise open knowledge model selection data selection diversity